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Computational Pathology.
Peter J. Schüffler, Qing Zhong, Peter J. Wild and Thomas J. Fuchs.
In: Johannes Haybäck (ed.) Mechanisms of Molecular Carcinogenesis - Volume 2, 1st ed. 2017 edition, Springer, ISBN 3-319-53660-5, 21. Jun. 2017
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@incollection{schuffler_computational_2017,
    edition = {1st ed. 2017 edition},
    title = {Computational {Pathology}},
    isbn = {3-319-53660-5},
    url = {http://www.springer.com/de/book/9783319536606},
    booktitle = {Mechanisms of {Molecular} {Carcinogenesis} - {Volume} 2},
    publisher = {Springer},
    author = {Sch\"uffler, Peter J. and Zhong, Qing and Wild, Peter J. and Fuchs, Thomas J.},
    editor = {Hayb\"ack, Johannes},
    month = jun,
    year = {2017}
}
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TY - CHAP
TI - Computational Pathology
AU - Schüffler, Peter J.
AU - Zhong, Qing
AU - Wild, Peter J.
AU - Fuchs, Thomas J.
T2 - Mechanisms of Molecular Carcinogenesis - Volume 2
A2 - Haybäck, Johannes
DA - 2017/06/21/
PY - 2017
ET - 1st ed. 2017 edition
PB - Springer
SN - 3-319-53660-5
UR - http://www.springer.com/de/book/9783319536606
ER -
Scaling of cytoskeletal organization with cell size in Drosophila.
Alison K. Spencer, Andrew J. Schaumberg and Jennifer A. Zallen.
Mol. Biol. Cell, p. mbc.E16-10-0691, 2017-04-12
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@article{spencer_scaling_2017,
    title = {Scaling of cytoskeletal organization with cell size in {Drosophila}},
    issn = {1059-1524, 1939-4586},
    url = {http://www.molbiolcell.org/content/early/2017/04/10/mbc.E16-10-0691},
    doi = {10.1091/mbc.E16-10-0691},
    abstract = {Spatially organized macromolecular complexes are essential for cell and tissue function, but the mechanisms that organize micron-scale structures within cells are not well understood. Microtubule-based structures such as mitotic spindles scale with cell size, but less is known about the scaling of actin structures within cells. Actin-rich denticle precursors cover the ventral surface of the Drosophila embryo and larva and provide templates for cuticular structures involved in larval locomotion. Using quantitative imaging and statistical modeling, we demonstrate that denticle number and spacing scale with cell size over a wide range of cell lengths in embryos and larvae. Denticle number and spacing are reduced under space-limited conditions, and both features robustly scale over a ten-fold increase in cell length during larval growth. We show that the relationship between cell length and denticle spacing can be recapitulated by specific mathematical equations in embryos and larvae, and that accurate denticle spacing requires an intact microtubule network and the microtubule minus-end-binding protein, Patronin. These results identify a novel mechanism of microtubule-dependent actin scaling that maintains precise patterns of actin organization during tissue growth.},
    language = {en},
    urldate = {2017-04-24TZ},
    journal = {Molecular Biology of the Cell},
    author = {Spencer, Alison K. and Schaumberg, Andrew J. and Zallen, Jennifer A.},
    month = apr,
    year = {2017},
    pages = {mbc.E16--10--0691}
}
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TY - JOUR
TI - Scaling of cytoskeletal organization with cell size in Drosophila
AU - Spencer, Alison K.
AU - Schaumberg, Andrew J.
AU - Zallen, Jennifer A.
T2 - Molecular Biology of the Cell
AB - Spatially organized macromolecular complexes are essential for cell and tissue function, but the mechanisms that organize micron-scale structures within cells are not well understood. Microtubule-based structures such as mitotic spindles scale with cell size, but less is known about the scaling of actin structures within cells. Actin-rich denticle precursors cover the ventral surface of the Drosophila embryo and larva and provide templates for cuticular structures involved in larval locomotion. Using quantitative imaging and statistical modeling, we demonstrate that denticle number and spacing scale with cell size over a wide range of cell lengths in embryos and larvae. Denticle number and spacing are reduced under space-limited conditions, and both features robustly scale over a ten-fold increase in cell length during larval growth. We show that the relationship between cell length and denticle spacing can be recapitulated by specific mathematical equations in embryos and larvae, and that accurate denticle spacing requires an intact microtubule network and the microtubule minus-end-binding protein, Patronin. These results identify a novel mechanism of microtubule-dependent actin scaling that maintains precise patterns of actin organization during tissue growth.
DA - 2017/04/12/
PY - 2017
DO - 10.1091/mbc.E16-10-0691
DP - www.molbiolcell.org
SP - mbc.E16
EP - 10-0691
J2 - Mol. Biol. Cell
LA - en
SN - 1059-1524, 1939-4586
UR - http://www.molbiolcell.org/content/early/2017/04/10/mbc.E16-10-0691
Y2 - 2017/04/24/T16:19:42Z
ER -
Spatially organized macromolecular complexes are essential for cell and tissue function, but the mechanisms that organize micron-scale structures within cells are not well understood. Microtubule-based structures such as mitotic spindles scale with cell size, but less is known about the scaling of actin structures within cells. Actin-rich denticle precursors cover the ventral surface of the Drosophila embryo and larva and provide templates for cuticular structures involved in larval locomotion. Using quantitative imaging and statistical modeling, we demonstrate that denticle number and spacing scale with cell size over a wide range of cell lengths in embryos and larvae. Denticle number and spacing are reduced under space-limited conditions, and both features robustly scale over a ten-fold increase in cell length during larval growth. We show that the relationship between cell length and denticle spacing can be recapitulated by specific mathematical equations in embryos and larvae, and that accurate denticle spacing requires an intact microtubule network and the microtubule minus-end-binding protein, Patronin. These results identify a novel mechanism of microtubule-dependent actin scaling that maintains precise patterns of actin organization during tissue growth.
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations.
Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo and Thomas J. Fuchs.
Proceedings of the 1st Machine Learning for Healthcare Conference, Machine Learning for Healthcare, vol. 56, p. 191-208, Proceedings of Machine Learning Research, PMLR, 18 Aug 2016
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@inproceedings{schuffler_mitochondria-based_2016,
    address = {Los Angeles},
    series = {Proceedings of {Machine} {Learning} {Research}},
    title = {Mitochondria-based {Renal} {Cell} {Carcinoma} {Subtyping}: {Learning} from {Deep} vs. {Flat} {Feature} {Representations}},
    volume = {56},
    url = {http://proceedings.mlr.press/v56/Schuffler16.html},
    abstract = {Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
    Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
    In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
    The best model reaches a cross-validation accuracy of 89\%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.},
    language = {English},
    booktitle = {Proceedings of the 1st {Machine} {Learning} for {Healthcare} {Conference}},
    publisher = {PMLR},
    author = {Sch\"uffler, Peter J. and Sarungbam, Judy and Muhammad, Hassan and Reznik, Ed and Tickoo, Satish K. and Fuchs, Thomas J.},
    editor = {Finale, Doshi-Valez and Fackler, Jim and Kale, David and Wallace, Byron and Weins, Jenna},
    month = aug,
    year = {2016},
    pages = {191--208}
}
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TY - CONF
TI - Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations
AU - Schüffler, Peter J.
AU - Sarungbam, Judy
AU - Muhammad, Hassan
AU - Reznik, Ed
AU - Tickoo, Satish K.
AU - Fuchs, Thomas J.
T2 - Machine Learning for Healthcare
A2 - Finale, Doshi-Valez
A2 - Fackler, Jim
A2 - Kale, David
A2 - Wallace, Byron
A2 - Weins, Jenna
T3 - Proceedings of Machine Learning Research
AB - Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
C1 - Los Angeles
C3 - Proceedings of the 1st Machine Learning for Healthcare Conference
DA - 2016/08/18/
PY - 2016
VL - 56
SP - 191
EP - 208
LA - English
PB - PMLR
UR - http://proceedings.mlr.press/v56/Schuffler16.html
ER -
Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative. Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification. In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN). The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes.
Stephanie R. Debats, Dee Luo, Lyndon D. Estes, Thomas J. Fuchs and Kelly K. Caylor.
Remote Sensing of Environment, vol. 179, p. 210-221, June 15, 2016
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@article{debats_generalized_2016,
    title = {A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes},
    volume = {179},
    issn = {0034-4257},
    url = {http://www.sciencedirect.com/science/article/pii/S0034425716301031},
    doi = {10.1016/j.rse.2016.03.010},
    abstract = {Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.},
    urldate = {2017-05-19TZ},
    journal = {Remote Sensing of Environment},
    author = {Debats, Stephanie R. and Luo, Dee and Estes, Lyndon D. and Fuchs, Thomas J. and Caylor, Kelly K.},
    month = jun,
    year = {2016},
    keywords = {Agriculture, Land cover, Sub-Saharan Africa, computer vision, machine learning},
    pages = {210--221}
}
Download Endnote/RIS citation
TY - JOUR
TI - A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes
AU - Debats, Stephanie R.
AU - Luo, Dee
AU - Estes, Lyndon D.
AU - Fuchs, Thomas J.
AU - Caylor, Kelly K.
T2 - Remote Sensing of Environment
AB - Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.
DA - 2016/06/15/
PY - 2016
DO - 10.1016/j.rse.2016.03.010
DP - ScienceDirect
VL - 179
SP - 210
EP - 221
J2 - Remote Sensing of Environment
SN - 0034-4257
UR - http://www.sciencedirect.com/science/article/pii/S0034425716301031
Y2 - 2017/05/19/T10:44:55Z
KW - Agriculture
KW - Land cover
KW - Sub-Saharan Africa
KW - computer vision
KW - machine learning
ER -
Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.
Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
Jakob M. Burgstaller, Peter J. Schüffler, Joachim M. Buhmann, Gustav Andreisek, Sebastian Winklhofer, Filippo Del Grande, Michèle Mattle, Florian Brunner, Georgios Karakoumis, Johann Steurer and Ulrike Held.
Spine, vol. 41, 17, p. 1053-1062, 2016
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{burgstaller_is_2016,
    title = {Is {There} an {Association} {Between} {Pain} and {Magnetic} {Resonance} {Imaging} {Parameters} in {Patients} {With} {Lumbar} {Spinal} {Stenosis}?},
    volume = {41},
    issn = {0362-2436},
    shorttitle = {Is {There} an {Association} {Between} {Pain} and {Magnetic} {Resonance} {Imaging} {Parameters} in {Patients} {With} {Lumbar} {Spinal} {Stenosis}?},
    url = {http://Insights.ovid.com/crossref?an=00007632-201609010-00015},
    doi = {10.1097/BRS.0000000000001544},
    abstract = {STUDY DESIGN:
    A prospective multicenter cohort study.
    OBJECTIVE:
    The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS).
    SUMMARY OF BACKGROUND DATA:
    At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear.
    METHODS:
    First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS).
    RESULTS:
    In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown.
    CONCLUSION:
    Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints.
    LEVEL OF EVIDENCE:
    2.},
    language = {en},
    number = {17},
    urldate = {2017-02-11TZ},
    journal = {Spine},
    author = {Burgstaller, Jakob M. and Sch\"uffler, Peter J. and Buhmann, Joachim M. and Andreisek, Gustav and Winklhofer, Sebastian and Del Grande, Filippo and Mattle, Mich\`ele and Brunner, Florian and Karakoumis, Georgios and Steurer, Johann and Held, Ulrike},
    year = {2016},
    pages = {1053--1062}
}
Download Endnote/RIS citation
TY - JOUR
TI - Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
AU - Burgstaller, Jakob M.
AU - Schüffler, Peter J.
AU - Buhmann, Joachim M.
AU - Andreisek, Gustav
AU - Winklhofer, Sebastian
AU - Del Grande, Filippo
AU - Mattle, Michèle
AU - Brunner, Florian
AU - Karakoumis, Georgios
AU - Steurer, Johann
AU - Held, Ulrike
T2 - Spine
AB - STUDY DESIGN:
A prospective multicenter cohort study.
OBJECTIVE:
The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS).
SUMMARY OF BACKGROUND DATA:
At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear.
METHODS:
First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS).
RESULTS:
In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown.
CONCLUSION:
Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints.
LEVEL OF EVIDENCE:
2.
DA - 2016///
PY - 2016
DO - 10.1097/BRS.0000000000001544
DP - CrossRef
VL - 41
IS - 17
SP - 1053
EP - 1062
LA - en
SN - 0362-2436
ST - Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
UR - http://Insights.ovid.com/crossref?an=00007632-201609010-00015
Y2 - 2017/02/11/T00:39:09Z
ER -
STUDY DESIGN: A prospective multicenter cohort study. OBJECTIVE: The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS). SUMMARY OF BACKGROUND DATA: At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear. METHODS: First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS). RESULTS: In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown. CONCLUSION: Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints. LEVEL OF EVIDENCE: 2.
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope.
Andrew J. Schaumberg, S. Joseph Sirintrapun, Hikmat A. Al-Ahmadie, Peter J. Schüffler and Thomas J. Fuchs.
13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics, CIBB, 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schaumberg_deepscope:_2016,
    address = {Stirling, United Kingdom},
    title = {{DeepScope}: {Nonintrusive} {Whole} {Slide} {Saliency} {Annotation} and {Prediction} from {Pathologists} at the {Microscope}},
    shorttitle = {{DeepScope}},
    url = {http://www.cs.stir.ac.uk/events/cibb2016/},
    abstract = {Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks.
    Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image.
    We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide.
    Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15\% in bladder and 91.50\% in prostate, with 75.00\% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.},
    language = {English},
    booktitle = {13th {International} {Conference} on {Computational} {Intelligence} methods for {Bioinformatics} and {Biostatistics}},
    author = {Schaumberg, Andrew J. and Sirintrapun, S. Joseph and Al-Ahmadie, Hikmat A. and Sch\"uffler, Peter J. and Fuchs, Thomas J.},
    year = {2016},
}
Download Endnote/RIS citation
TY - CONF
TI - DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope
AU - Schaumberg, Andrew J.
AU - Sirintrapun, S. Joseph
AU - Al-Ahmadie, Hikmat A.
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
T2 - CIBB
AB - Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks.
Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image.
We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide.
Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.
C1 - Stirling, United Kingdom
C3 - 13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
DA - 2016///
PY - 2016
LA - English
ST - DeepScope
UR - http://www.cs.stir.ac.uk/events/cibb2016/
ER -
Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks. Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image. We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide. Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.
Big Data in der Medizin.
Qing Zhong, Roman Barnert, Gunnar Ratsch, Thomas J. Fuchs and Peter J. Wild.
Leading Opinions: Hämatologie & Onkologie, p. 102-105, 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{zhong_big_2016,
    title = {Big {Data} in der {Medizin}},
    abstract = {IT-Systeme in Krankenh\"ausern bereiten Unmengen von Daten aus unterschiedlichen Quellen, die von spezialisierten interdisziplin\"aren Teams gewonnen worden sind, zu wertvollen Informationen auf. Durch die Ausweitung der Analysen auf mehrere Tausend anonymisierte Patienten und die Vernetzung mit anderen Kliniken und Forschungszentren wird ein \"Okosystem zum Wohle des Patienten geschaffen. Dieses auf Big-Data-Ans\"atzen gest\"utzte, evidenzbasierte und patientenorientierte Gesundheitswesen ist in naher Zukunft Realit\"at.},
    language = {German},
    journal = {Leading Opinions: H\"amatologie \& Onkologie},
    author = {Zhong, Qing and Barnert, Roman and Ratsch, Gunnar and Fuchs, Thomas J. and Wild, Peter J.},
    year = {2016},
    pages = {102--105}
}
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TY - NEWS
TI - Big Data in der Medizin
AU - Zhong, Qing
AU - Barnert, Roman
AU - Ratsch, Gunnar
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
T2 - Leading Opinions: Hämatologie & Onkologie
AB - IT-Systeme in Krankenhäusern bereiten Unmengen von Daten aus unterschiedlichen Quellen, die von spezialisierten interdisziplinären Teams gewonnen worden sind, zu wertvollen Informationen auf. Durch die Ausweitung der Analysen auf mehrere Tausend anonymisierte Patienten und die Vernetzung mit anderen Kliniken und Forschungszentren wird ein Ökosystem zum Wohle des Patienten geschaffen. Dieses auf Big-Data-Ansätzen gestützte, evidenzbasierte und patientenorientierte Gesundheitswesen ist in naher Zukunft Realität.
DA - 2016///
PY - 2016
SP - 102
EP - 105
LA - German
ER -
IT-Systeme in Krankenhäusern bereiten Unmengen von Daten aus unterschiedlichen Quellen, die von spezialisierten interdisziplinären Teams gewonnen worden sind, zu wertvollen Informationen auf. Durch die Ausweitung der Analysen auf mehrere Tausend anonymisierte Patienten und die Vernetzung mit anderen Kliniken und Forschungszentren wird ein Ökosystem zum Wohle des Patienten geschaffen. Dieses auf Big-Data-Ansätzen gestützte, evidenzbasierte und patientenorientierte Gesundheitswesen ist in naher Zukunft Realität.
Cancer-secreted AGR2 induces programmed cell death in normal cells.
Elizabeth A. Vitello, Sue-Ing Quek, Heather Kincaid, Thomas Fuchs, Daniel J. Crichton, Pamela Troisch and Alvin Y. Liu.
Oncotarget, vol. 5, 0, 2016
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@article{vitello_cancer-secreted_2016,
    title = {Cancer-secreted {AGR}2 induces programmed cell death in normal cells},
    volume = {5},
    issn = {1949-2553},
    url = {http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget&page=article&op=view&path%5B%5D=9921},
    abstract = {Anterior Gradient 2 (AGR2) is a protein expressed in many solid tumor types including prostate, pancreatic, breast and lung. AGR2 functions as a protein disulfide isomerase in the endoplasmic reticulum. However, AGR2 is secreted by cancer cells that overexpress this molecule. Secretion of AGR2 was also found in salamander limb regeneration. Due to its ubiquity, tumor secretion of AGR2 must serve an important role in cancer, yet its molecular function is largely unknown. This study examined the effect of cancer-secreted AGR2 on normal cells. Prostate stromal cells were cultured, and tissue digestion media containing AGR2 prepared from prostate primary cancer 10-076 CP and adenocarcinoma LuCaP 70CR xenograft were added. The control were tissue digestion media containing no AGR2 prepared from benign prostate 10-076 NP and small cell carcinoma LuCaP 145.1 xenograft. In the presence of tumor-secreted AGR2, the stromal cells were found to undergo programmed cell death (PCD) characterized by formation of cellular blebs, cell shrinkage, and DNA fragmentation as seen when the stromal cells were UV irradiated or treated by a pro-apoptotic drug. PCD could be prevented with the addition of the monoclonal AGR2-neutralizing antibody P3A5. DNA microarray analysis of LuCaP 70CR media-treated vs . LuCaP 145.1 media-treated cells showed downregulation of the gene SAT1 as a major change in cells exposed to AGR2. RT-PCR analysis confirmed the array result. SAT1 encodes spermidine/spermine N 1 -acetyltransferase, which maintains intracellular polyamine levels. Abnormal polyamine metabolism as a result of altered SAT1 activity has an adverse effect on cells through the induction of PCD.},
    number = {0},
    urldate = {2016-06-16TZ},
    journal = {Oncotarget},
    author = {Vitello, Elizabeth A. and Quek, Sue-Ing and Kincaid, Heather and Fuchs, Thomas and Crichton, Daniel J. and Troisch, Pamela and Liu, Alvin Y.},
    year = {2016},
}
Download Endnote/RIS citation
TY - JOUR
TI - Cancer-secreted AGR2 induces programmed cell death in normal cells
AU - Vitello, Elizabeth A.
AU - Quek, Sue-Ing
AU - Kincaid, Heather
AU - Fuchs, Thomas
AU - Crichton, Daniel J.
AU - Troisch, Pamela
AU - Liu, Alvin Y.
T2 - Oncotarget
AB - Anterior Gradient 2 (AGR2) is a protein expressed in many solid tumor types including prostate, pancreatic, breast and lung. AGR2 functions as a protein disulfide isomerase in the endoplasmic reticulum. However, AGR2 is secreted by cancer cells that overexpress this molecule. Secretion of AGR2 was also found in salamander limb regeneration. Due to its ubiquity, tumor secretion of AGR2 must serve an important role in cancer, yet its molecular function is largely unknown. This study examined the effect of cancer-secreted AGR2 on normal cells. Prostate stromal cells were cultured, and tissue digestion media containing AGR2 prepared from prostate primary cancer 10-076 CP and adenocarcinoma LuCaP 70CR xenograft were added. The control were tissue digestion media containing no AGR2 prepared from benign prostate 10-076 NP and small cell carcinoma LuCaP 145.1 xenograft. In the presence of tumor-secreted AGR2, the stromal cells were found to undergo programmed cell death (PCD) characterized by formation of cellular blebs, cell shrinkage, and DNA fragmentation as seen when the stromal cells were UV irradiated or treated by a pro-apoptotic drug. PCD could be prevented with the addition of the monoclonal AGR2-neutralizing antibody P3A5. DNA microarray analysis of LuCaP 70CR media-treated vs . LuCaP 145.1 media-treated cells showed downregulation of the gene SAT1 as a major change in cells exposed to AGR2. RT-PCR analysis confirmed the array result. SAT1 encodes spermidine/spermine N 1 -acetyltransferase, which maintains intracellular polyamine levels. Abnormal polyamine metabolism as a result of altered SAT1 activity has an adverse effect on cells through the induction of PCD.
DA - 2016///
PY - 2016
DP - www.impactjournals.com
VL - 5
IS - 0
SN - 1949-2553
UR - http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget&page=article&op=view&path%5B%5D=9921
Y2 - 2016/06/16/T02:00:19Z
ER -
Anterior Gradient 2 (AGR2) is a protein expressed in many solid tumor types including prostate, pancreatic, breast and lung. AGR2 functions as a protein disulfide isomerase in the endoplasmic reticulum. However, AGR2 is secreted by cancer cells that overexpress this molecule. Secretion of AGR2 was also found in salamander limb regeneration. Due to its ubiquity, tumor secretion of AGR2 must serve an important role in cancer, yet its molecular function is largely unknown. This study examined the effect of cancer-secreted AGR2 on normal cells. Prostate stromal cells were cultured, and tissue digestion media containing AGR2 prepared from prostate primary cancer 10-076 CP and adenocarcinoma LuCaP 70CR xenograft were added. The control were tissue digestion media containing no AGR2 prepared from benign prostate 10-076 NP and small cell carcinoma LuCaP 145.1 xenograft. In the presence of tumor-secreted AGR2, the stromal cells were found to undergo programmed cell death (PCD) characterized by formation of cellular blebs, cell shrinkage, and DNA fragmentation as seen when the stromal cells were UV irradiated or treated by a pro-apoptotic drug. PCD could be prevented with the addition of the monoclonal AGR2-neutralizing antibody P3A5. DNA microarray analysis of LuCaP 70CR media-treated vs . LuCaP 145.1 media-treated cells showed downregulation of the gene SAT1 as a major change in cells exposed to AGR2. RT-PCR analysis confirmed the array result. SAT1 encodes spermidine/spermine N 1 -acetyltransferase, which maintains intracellular polyamine levels. Abnormal polyamine metabolism as a result of altered SAT1 activity has an adverse effect on cells through the induction of PCD.
Multi-Organ Cancer Classification and Survival Analysis.
Stefan Bauer, Nicolas Carion, Peter Schüffler, Thomas Fuchs, Peter Wild and Joachim M. Buhmann.
arXiv:1606.00897 [cs, q-bio, stat], 2016
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@article{bauer_multi-organ_2016,
    title = {Multi-{Organ} {Cancer} {Classification} and {Survival} {Analysis}},
    url = {http://arxiv.org/abs/1606.00897},
    abstract = {Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist (\$p=0.006\$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.},
    urldate = {2016-06-16TZ},
    journal = {arXiv:1606.00897 [cs, q-bio, stat]},
    author = {Bauer, Stefan and Carion, Nicolas and Sch\"uffler, Peter and Fuchs, Thomas and Wild, Peter and Buhmann, Joachim M.},
    year = {2016},
    keywords = {Computer Science - Learning, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs, Statistics - Machine Learning}
}
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TY - JOUR
TI - Multi-Organ Cancer Classification and Survival Analysis
AU - Bauer, Stefan
AU - Carion, Nicolas
AU - Schüffler, Peter
AU - Fuchs, Thomas
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - arXiv:1606.00897 [cs, q-bio, stat]
AB - Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist ($p=0.006$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.
DA - 2016///
PY - 2016
DP - arXiv.org
UR - http://arxiv.org/abs/1606.00897
Y2 - 2016/06/16/T01:52:59Z
KW - Computer Science - Learning
KW - Quantitative Biology - Quantitative Methods
KW - Quantitative Biology - Tissues and Organs
KW - Statistics - Machine Learning
ER -
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist ($p=0.006$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.
Classifying Renal Cell Carcinoma by Using Convolutional Neural Networks to Deconstruct Pathological Images.
Hassan Muhammad, Peter J. Schüffler, Judy Sarungbam, Satish K. Tickoo and Thomas Fuchs.
Vincent du Vigneaud Memorial Research Symposium, 2016
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@misc{muhammad_classifying_2016,
    address = {Weill Cornell Medicine},
    type = {Poster},
    title = {Classifying {Renal} {Cell} {Carcinoma} by {Using} {Convolutional} {Neural} {Networks} to {Deconstruct} {Pathological} {Images}.},
    author = {Muhammad, Hassan},
    collaborator = {Sch\"uffler, Peter J. and Sarungbam, Judy and Tickoo, Satish K. and Fuchs, Thomas},
    year = {2016},
}
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TY - SLIDE
TI - Classifying Renal Cell Carcinoma by Using Convolutional Neural Networks to Deconstruct Pathological Images.
T2 - Vincent du Vigneaud Memorial Research Symposium
A2 - Muhammad, Hassan
CY - Weill Cornell Medicine
DA - 2016///
PY - 2016
M3 - Poster
ER -
Image-Based Computational Quantification and Visualization of Genetic Alterations and Tumour Heterogeneity.
Qing Zhong, Jan H. Rüschoff, Tiannan Guo, Maria Gabrani, Peter J. Schüffler, Markus Rechsteiner, Yansheng Liu, Thomas J. Fuchs, Niels J. Rupp, Christian Fankhauser, Joachim M. Buhmann, Sven Perner, Cédric Poyet, Miriam Blattner, Davide Soldini, Holger Moch, Mark A. Rubin, Aurelia Noske, Josef Rüschoff, Michael C. Haffner, Wolfram Jochum and Peter J. Wild.
Scientific Reports, vol. 6, p. 24146, 2016
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@article{zhong_image-based_2016,
    title = {Image-{Based} {Computational} {Quantification} and {Visualization} of {Genetic} {Alterations} and {Tumour} {Heterogeneity}},
    volume = {6},
    issn = {2045-2322},
    url = {http://www.nature.com/articles/srep24146},
    doi = {10.1038/srep24146},
    urldate = {2016-04-12TZ},
    journal = {Scientific Reports},
    author = {Zhong, Qing and R\"uschoff, Jan H. and Guo, Tiannan and Gabrani, Maria and Sch\"uffler, Peter J. and Rechsteiner, Markus and Liu, Yansheng and Fuchs, Thomas J. and Rupp, Niels J. and Fankhauser, Christian and Buhmann, Joachim M. and Perner, Sven and Poyet, C\'edric and Blattner, Miriam and Soldini, Davide and Moch, Holger and Rubin, Mark A. and Noske, Aurelia and R\"uschoff, Josef and Haffner, Michael C. and Jochum, Wolfram and Wild, Peter J.},
    year = {2016},
    pages = {24146}
}
Download Endnote/RIS citation
TY - JOUR
TI - Image-Based Computational Quantification and Visualization of Genetic Alterations and Tumour Heterogeneity
AU - Zhong, Qing
AU - Rüschoff, Jan H.
AU - Guo, Tiannan
AU - Gabrani, Maria
AU - Schüffler, Peter J.
AU - Rechsteiner, Markus
AU - Liu, Yansheng
AU - Fuchs, Thomas J.
AU - Rupp, Niels J.
AU - Fankhauser, Christian
AU - Buhmann, Joachim M.
AU - Perner, Sven
AU - Poyet, Cédric
AU - Blattner, Miriam
AU - Soldini, Davide
AU - Moch, Holger
AU - Rubin, Mark A.
AU - Noske, Aurelia
AU - Rüschoff, Josef
AU - Haffner, Michael C.
AU - Jochum, Wolfram
AU - Wild, Peter J.
T2 - Scientific Reports
DA - 2016///
PY - 2016
DO - 10.1038/srep24146
DP - CrossRef
VL - 6
SP - 24146
SN - 2045-2322
UR - http://www.nature.com/articles/srep24146
Y2 - 2016/04/12/T01:49:30Z
ER -
Autonomous Terrain Classification With Co- and Self-Training Approach.
K. Otsu, M. Ono, T. J. Fuchs, I. Baldwin and T. Kubota.
IEEE Robotics and Automation Letters, vol. 1, 2, p. 814-819, 2016
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@article{otsu_autonomous_2016,
    title = {Autonomous {Terrain} {Classification} {With} {Co}- and {Self}-{Training} {Approach}},
    volume = {1},
    issn = {2377-3766},
    url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7397920},
    doi = {10.1109/LRA.2016.2525040},
    abstract = {Identifying terrain type is crucial to safely operating planetary exploration rovers. Vision-based terrain classifiers, which are typically trained by thousands of labeled images using machine learning methods, have proven to be particularly successful. However, since planetary rovers are to boldly go where no one has gone before, training data are usually not available a priori; instead, rovers have to quickly learn from their own experiences in an early phase of surface operation. This research addresses the challenge by combining two key ideas. The first idea is to use both onboard imagery and vibration data, and let rovers learn from physical experiences through self-supervised learning. The underlying fact is that visually similar terrain may be disambiguated by mechanical vibrations. The second idea is to employ the co- and self-training approaches. The idea of co-training is to train two classifiers separately for vision and vibration data, and re-train them iteratively on each other's output. Meanwhile, the self-training approach, applied only to the vision-based classifier, re-trains the classifier on its own output. Both approaches essentially increase the amount of labels, hence enable the terrain classifiers to operate from a sparse training dataset. The proposed approach was validated with a four-wheeled test rover in Mars-analogous terrain, including bedrock, soil, and sand. The co-training setup based on support vector machines with color and wavelet-based features successfully estimated terrain types with 82\% accuracy with only three labeled images.},
    number = {2},
    journal = {IEEE Robotics and Automation Letters},
    author = {Otsu, K. and Ono, M. and Fuchs, T. J. and Baldwin, I. and Kubota, T.},
    year = {2016},
    keywords = {Image color analysis, Mars, Mars-analogous terrain, Semantic Scene Understanding, Soil, Space Robotics, Support vector machines, Training, Training data, Visual Learning, Wheels, aerospace computing, autonomous terrain classification, co-training, color features, four-wheeled test rover, image classification, image colour analysis, learning (artificial intelligence), mechanical vibrations, onboard imagery, planetary rovers, self-supervised learning, self-training, terrain mapping, vibration data, vibrations, vision-based classifier, wavelet transforms, wavelet-based features},
    pages = {814--819}
}
Download Endnote/RIS citation
TY - JOUR
TI - Autonomous Terrain Classification With Co- and Self-Training Approach
AU - Otsu, K.
AU - Ono, M.
AU - Fuchs, T. J.
AU - Baldwin, I.
AU - Kubota, T.
T2 - IEEE Robotics and Automation Letters
AB - Identifying terrain type is crucial to safely operating planetary exploration rovers. Vision-based terrain classifiers, which are typically trained by thousands of labeled images using machine learning methods, have proven to be particularly successful. However, since planetary rovers are to boldly go where no one has gone before, training data are usually not available a priori; instead, rovers have to quickly learn from their own experiences in an early phase of surface operation. This research addresses the challenge by combining two key ideas. The first idea is to use both onboard imagery and vibration data, and let rovers learn from physical experiences through self-supervised learning. The underlying fact is that visually similar terrain may be disambiguated by mechanical vibrations. The second idea is to employ the co- and self-training approaches. The idea of co-training is to train two classifiers separately for vision and vibration data, and re-train them iteratively on each other's output. Meanwhile, the self-training approach, applied only to the vision-based classifier, re-trains the classifier on its own output. Both approaches essentially increase the amount of labels, hence enable the terrain classifiers to operate from a sparse training dataset. The proposed approach was validated with a four-wheeled test rover in Mars-analogous terrain, including bedrock, soil, and sand. The co-training setup based on support vector machines with color and wavelet-based features successfully estimated terrain types with 82% accuracy with only three labeled images.
DA - 2016///
PY - 2016
DO - 10.1109/LRA.2016.2525040
DP - IEEE Xplore
VL - 1
IS - 2
SP - 814
EP - 819
SN - 2377-3766
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7397920
KW - Image color analysis
KW - Mars
KW - Mars-analogous terrain
KW - Semantic Scene Understanding
KW - Soil
KW - Space Robotics
KW - Support vector machines
KW - Training
KW - Training data
KW - Visual Learning
KW - Wheels
KW - aerospace computing
KW - autonomous terrain classification
KW - co-training
KW - color features
KW - four-wheeled test rover
KW - image classification
KW - image colour analysis
KW - learning (artificial intelligence)
KW - mechanical vibrations
KW - onboard imagery
KW - planetary rovers
KW - self-supervised learning
KW - self-training
KW - terrain mapping
KW - vibration data
KW - vibrations
KW - vision-based classifier
KW - wavelet transforms
KW - wavelet-based features
ER -
Identifying terrain type is crucial to safely operating planetary exploration rovers. Vision-based terrain classifiers, which are typically trained by thousands of labeled images using machine learning methods, have proven to be particularly successful. However, since planetary rovers are to boldly go where no one has gone before, training data are usually not available a priori; instead, rovers have to quickly learn from their own experiences in an early phase of surface operation. This research addresses the challenge by combining two key ideas. The first idea is to use both onboard imagery and vibration data, and let rovers learn from physical experiences through self-supervised learning. The underlying fact is that visually similar terrain may be disambiguated by mechanical vibrations. The second idea is to employ the co- and self-training approaches. The idea of co-training is to train two classifiers separately for vision and vibration data, and re-train them iteratively on each other's output. Meanwhile, the self-training approach, applied only to the vision-based classifier, re-trains the classifier on its own output. Both approaches essentially increase the amount of labels, hence enable the terrain classifiers to operate from a sparse training dataset. The proposed approach was validated with a four-wheeled test rover in Mars-analogous terrain, including bedrock, soil, and sand. The co-training setup based on support vector machines with color and wavelet-based features successfully estimated terrain types with 82% accuracy with only three labeled images.
Predicting Drug Interactions and Mutagenicity with Ensemble Classifiers on Subgraphs of Molecules.
Andrew Schaumberg, Angela Yu, Tatsuhiro Koshi, Xiaochan Zong and Santoshkalyan Rayadhurgam.
arXiv:1601.07233 [cs, stat], 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{schaumberg_predicting_2016,
    title = {Predicting {Drug} {Interactions} and {Mutagenicity} with {Ensemble} {Classifiers} on {Subgraphs} of {Molecules}},
    url = {http://arxiv.org/abs/1601.07233},
    abstract = {In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this problem for their generality to all types of molecules, due to the inherent association of atoms through atomic bonds. Subgraphs can represent different molecular domains. These domains can be biologically significant as most molecules only have portions that are of functional significance and can interact with other domains. Thus, we use subgraphs as features in different machine learning algorithms to predict if two drugs interact and predict potential single molecule effects.},
    urldate = {2016-01-28TZ},
    journal = {arXiv:1601.07233 [cs, stat]},
    author = {Schaumberg, Andrew and Yu, Angela and Koshi, Tatsuhiro and Zong, Xiaochan and Rayadhurgam, Santoshkalyan},
    year = {2016},
    keywords = {Computer Science - Learning, I.2.1, J.3, Statistics - Machine Learning}
}
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TY - JOUR
TI - Predicting Drug Interactions and Mutagenicity with Ensemble Classifiers on Subgraphs of Molecules
AU - Schaumberg, Andrew
AU - Yu, Angela
AU - Koshi, Tatsuhiro
AU - Zong, Xiaochan
AU - Rayadhurgam, Santoshkalyan
T2 - arXiv:1601.07233 [cs, stat]
AB - In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this problem for their generality to all types of molecules, due to the inherent association of atoms through atomic bonds. Subgraphs can represent different molecular domains. These domains can be biologically significant as most molecules only have portions that are of functional significance and can interact with other domains. Thus, we use subgraphs as features in different machine learning algorithms to predict if two drugs interact and predict potential single molecule effects.
DA - 2016///
PY - 2016
DP - arXiv.org
UR - http://arxiv.org/abs/1601.07233
Y2 - 2016/01/28/T19:24:38Z
KW - Computer Science - Learning
KW - I.2.1
KW - J.3
KW - Statistics - Machine Learning
ER -
In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this problem for their generality to all types of molecules, due to the inherent association of atoms through atomic bonds. Subgraphs can represent different molecular domains. These domains can be biologically significant as most molecules only have portions that are of functional significance and can interact with other domains. Thus, we use subgraphs as features in different machine learning algorithms to predict if two drugs interact and predict potential single molecule effects.
Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys.
S. G. Djorgovski, M. J. Graham, C. Donalek, A. A. Mahabal, A. J. Drake, M. Turmon and T.J. Fuchs.
Future Generation Computer Systems, 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{djorgovski_real-time_2016,
    title = {Real-{Time} {Data} {Mining} of {Massive} {Data} {Streams} from {Synoptic} {Sky} {Surveys}},
    issn = {0167-739X},
    url = {http://www.sciencedirect.com/science/article/pii/S0167739X1500326X},
    doi = {10.1016/j.future.2015.10.013},
    abstract = {The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.},
    urldate = {2016-01-20TZ},
    journal = {Future Generation Computer Systems},
    author = {Djorgovski, S. G. and Graham, M. J. and Donalek, C. and Mahabal, A. A. and Drake, A. J. and Turmon, M. and Fuchs, T.J.},
    year = {2016},
    keywords = {Automated decision making, Bayesian methods, Massive data streams, Sky surveys, machine learning}
}
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TY - JOUR
TI - Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys
AU - Djorgovski, S. G.
AU - Graham, M. J.
AU - Donalek, C.
AU - Mahabal, A. A.
AU - Drake, A. J.
AU - Turmon, M.
AU - Fuchs, T.J.
T2 - Future Generation Computer Systems
AB - The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
DA - 2016///
PY - 2016
DO - 10.1016/j.future.2015.10.013
DP - ScienceDirect
J2 - Future Generation Computer Systems
SN - 0167-739X
UR - http://www.sciencedirect.com/science/article/pii/S0167739X1500326X
Y2 - 2016/01/20/T14:32:18Z
KW - Automated decision making
KW - Bayesian methods
KW - Massive data streams
KW - Sky surveys
KW - machine learning
ER -
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
Automatic single cell segmentation on highly multiplexed tissue images.
Peter J. Schüffler, Denis Schapiro, Charlotte Giesen, Hao A. O. Wang, Bernd Bodenmiller and Joachim M. Buhmann.
Cytometry Part A, vol. 87, 10, p. 936-942, 10/2015
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_automatic_2015,
    title = {Automatic single cell segmentation on highly multiplexed tissue images},
    volume = {87},
    issn = {15524922},
    shorttitle = {Automatic single cell segmentation on highly multiplexed tissue images},
    url = {http://doi.wiley.com/10.1002/cyto.a.22702},
    doi = {10.1002/cyto.a.22702},
    abstract = {The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.},
    language = {en},
    number = {10},
    urldate = {2017-02-14TZ},
    journal = {Cytometry Part A},
    author = {Sch\"uffler, Peter J. and Schapiro, Denis and Giesen, Charlotte and Wang, Hao A. O. and Bodenmiller, Bernd and Buhmann, Joachim M.},
    month = oct,
    year = {2015},
    pages = {936--942}
}
Download Endnote/RIS citation
TY - JOUR
TI - Automatic single cell segmentation on highly multiplexed tissue images
AU - Schüffler, Peter J.
AU - Schapiro, Denis
AU - Giesen, Charlotte
AU - Wang, Hao A. O.
AU - Bodenmiller, Bernd
AU - Buhmann, Joachim M.
T2 - Cytometry Part A
AB - The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.
DA - 2015/10//
PY - 2015
DO - 10.1002/cyto.a.22702
DP - CrossRef
VL - 87
IS - 10
SP - 936
EP - 942
LA - en
SN - 15524922
ST - Automatic single cell segmentation on highly multiplexed tissue images
UR - http://doi.wiley.com/10.1002/cyto.a.22702
Y2 - 2017/02/14/T19:42:24Z
ER -
The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.
Understanding Neural Networks Through Deep Visualization.
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs and Hod Lipson.
ICML Deep Learning Workshop, 2015
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@inproceedings{yosinski_understanding_2015,
    title = {Understanding {Neural} {Networks} {Through} {Deep} {Visualization}},
    url = {http://arxiv.org/abs/1506.06579},
    abstract = {Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup.},
    urldate = {2015-12-03TZ},
    booktitle = {{ICML} {Deep} {Learning} {Workshop}},
    author = {Yosinski, Jason and Clune, Jeff and Nguyen, Anh and Fuchs, Thomas and Lipson, Hod},
    year = {2015},
}
Download Endnote/RIS citation
TY - CONF
TI - Understanding Neural Networks Through Deep Visualization
AU - Yosinski, Jason
AU - Clune, Jeff
AU - Nguyen, Anh
AU - Fuchs, Thomas
AU - Lipson, Hod
AB - Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup.
C3 - ICML Deep Learning Workshop
DA - 2015///
PY - 2015
DP - Google Scholar
UR - http://arxiv.org/abs/1506.06579
Y2 - 2015/12/03/T01:13:42Z
ER -
Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup.
Enhanced Flyby Science with Onboard Computer Vision: Tracking and Surface Feature Detection at Small Bodies.
Thomas J. Fuchs, David R. Thompson, Brian D. Bue, Julie Castillo-Rogez, Steve A. Chien, Dero Gharibian and Kiri L. Wagstaff.
Earth and Space Science, vol. 2, 10, p. 417-34, 2015
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@article{fuchs_enhanced_2015,
    title = {Enhanced {Flyby} {Science} with {Onboard} {Computer} {Vision}: {Tracking} and {Surface} {Feature} {Detection} at {Small} {Bodies}},
    volume = {2},
    issn = {2333-5084},
    shorttitle = {Enhanced flyby science with onboard computer vision},
    url = {http://onlinelibrary.wiley.com/doi/10.1002/2014EA000042/abstract},
    doi = {10.1002/2014EA000042},
    abstract = {Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.},
    language = {en},
    number = {10},
    urldate = {2015-11-22TZ},
    journal = {Earth and Space Science},
    author = {Fuchs, Thomas J. and Thompson, David R. and Bue, Brian D. and Castillo-Rogez, Julie and Chien, Steve A. and Gharibian, Dero and Wagstaff, Kiri L.},
    year = {2015},
    keywords = {0540 Image processing, 0555 Neural networks, fuzzy logic, machine learning, 6055 Surfaces, 6094 Instruments and techniques, 6205 Asteroids, asteroids, comets, computer vision, flyby, machine learning, small bodies},
    pages = {417--34}
}
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TY - JOUR
TI - Enhanced Flyby Science with Onboard Computer Vision: Tracking and Surface Feature Detection at Small Bodies
AU - Fuchs, Thomas J.
AU - Thompson, David R.
AU - Bue, Brian D.
AU - Castillo-Rogez, Julie
AU - Chien, Steve A.
AU - Gharibian, Dero
AU - Wagstaff, Kiri L.
T2 - Earth and Space Science
AB - Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.
DA - 2015///
PY - 2015
DO - 10.1002/2014EA000042
DP - Wiley Online Library
VL - 2
IS - 10
SP - 417
EP - 34
J2 - Earth and Space Science
LA - en
SN - 2333-5084
ST - Enhanced flyby science with onboard computer vision
UR - http://onlinelibrary.wiley.com/doi/10.1002/2014EA000042/abstract
Y2 - 2015/11/22/T21:25:18Z
KW - 0540 Image processing
KW - 0555 Neural networks, fuzzy logic, machine learning
KW - 6055 Surfaces
KW - 6094 Instruments and techniques
KW - 6205 Asteroids
KW - asteroids
KW - comets
KW - computer vision
KW - flyby
KW - machine learning
KW - small bodies
ER -
Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.
Boosting Convolutional Features for Robust Object Proposals.
Nikolaos Karianakis, Thomas J. Fuchs and Stefano Soatto.
2015
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@article{karianakis_boosting_2015,
    title = {Boosting {Convolutional} {Features} for {Robust} {Object} {Proposals}},
    url = {http://arxiv.org/abs/1503.06350},
    author = {Karianakis, Nikolaos and Fuchs, Thomas J. and Soatto, Stefano},
    year = {2015},
}
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TY - JOUR
TI - Boosting Convolutional Features for Robust Object Proposals
AU - Karianakis, Nikolaos
AU - Fuchs, Thomas J.
AU - Soatto, Stefano
DA - 2015///
PY - 2015
UR - http://arxiv.org/abs/1503.06350
ER -
Machine Learning Approaches to Analyze Histological Images of Tissues from Radical Prostatectomies.
Arkadiusz Gertych, Nathan Ing, Zhaoxuan Ma, Thomas J. Fuchs, Sadri Salman, Sambit Mohanty, Sanica Bhele, Adriana Velásquez-Vacca, Mahul B. Amin and Beatrice S. Knudsen.
Computerized Medical Imaging and Graphics, vol. 46, Part 2, p. 197-208, Information Technologies in Biomedicine, 2015
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@article{gertych_machine_2015,
    series = {Information {Technologies} in {Biomedicine}},
    title = {Machine {Learning} {Approaches} to {Analyze} {Histological} {Images} of {Tissues} from {Radical} {Prostatectomies}},
    volume = {46, Part 2},
    issn = {0895-6111},
    url = {http://www.sciencedirect.com/science/article/pii/S0895611115001184},
    doi = {10.1016/j.compmedimag.2015.08.002},
    abstract = {Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H\&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n = 19) and test (n = 191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN + PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J = 59.5 ± 14.6 and Rand Ri = 62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN = 35.2 ± 24.9, OBN = 49.6 ± 32, JPCa = 49.5 ± 18.5, OPCa = 72.7 ± 14.8 and Ri = 60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.},
    urldate = {2016-01-03TZ},
    journal = {Computerized Medical Imaging and Graphics},
    author = {Gertych, Arkadiusz and Ing, Nathan and Ma, Zhaoxuan and Fuchs, Thomas J. and Salman, Sadri and Mohanty, Sambit and Bhele, Sanica and Vel\'asquez-Vacca, Adriana and Amin, Mahul B. and Knudsen, Beatrice S.},
    year = {2015},
    keywords = {Image analysis, Prostate cancer, Tissue classification, Tissue quantification, machine learning},
    pages = {197--208}
}
Download Endnote/RIS citation
TY - JOUR
TI - Machine Learning Approaches to Analyze Histological Images of Tissues from Radical Prostatectomies
AU - Gertych, Arkadiusz
AU - Ing, Nathan
AU - Ma, Zhaoxuan
AU - Fuchs, Thomas J.
AU - Salman, Sadri
AU - Mohanty, Sambit
AU - Bhele, Sanica
AU - Velásquez-Vacca, Adriana
AU - Amin, Mahul B.
AU - Knudsen, Beatrice S.
T2 - Computerized Medical Imaging and Graphics
T3 - Information Technologies in Biomedicine
AB - Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n = 19) and test (n = 191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN + PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J = 59.5 ± 14.6 and Rand Ri = 62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN = 35.2 ± 24.9, OBN = 49.6 ± 32, JPCa = 49.5 ± 18.5, OPCa = 72.7 ± 14.8 and Ri = 60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.
DA - 2015///
PY - 2015
DO - 10.1016/j.compmedimag.2015.08.002
DP - ScienceDirect
VL - 46, Part 2
SP - 197
EP - 208
J2 - Computerized Medical Imaging and Graphics
SN - 0895-6111
UR - http://www.sciencedirect.com/science/article/pii/S0895611115001184
Y2 - 2016/01/03/T16:12:47Z
KW - Image analysis
KW - Prostate cancer
KW - Tissue classification
KW - Tissue quantification
KW - machine learning
ER -
Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n = 19) and test (n = 191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN + PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J = 59.5 ± 14.6 and Rand Ri = 62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN = 35.2 ± 24.9, OBN = 49.6 ± 32, JPCa = 49.5 ± 18.5, OPCa = 72.7 ± 14.8 and Ri = 60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.
Risk-aware Planetary Rover Operation: Autonomous Terrain Classification and Path Planning.
Masahiro Ono, Thomas J. Fuchs, Amanda Steffy, Mark Maimone and Jeng Yen.
Proceedings of the 36th IEEE Aerospace Conference, p. 1–10, 2015
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{ono_risk-aware_2015,
    title = {Risk-aware {Planetary} {Rover} {Operation}: {Autonomous} {Terrain} {Classification} and {Path} {Planning}},
    url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7119022&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7119022},
    doi = {10.1109/AERO.2015.7119022},
    abstract = {Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed ground-based Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers. The risk-aware rover operation tool is built upon two technologies. The first technology is a machine learning-based terrain classification that is capable of identifying potential hazards, such as pointy rocks and soft terrains, from images. The second technology is a risk-aware path planner based on rapidly-exploring random graph (RRG) and the A* search algorithms, which is capable of avoiding hazards identified by the terrain classifier with explicitly considering wheel placement. We demonstrate the integrated capability of the proposed risk-aware rover operation tool by using the images taken by the Curiosity rover.},
    booktitle = {Proceedings of the 36th {IEEE} {Aerospace} {Conference}},
    author = {Ono, Masahiro and Fuchs, Thomas J. and Steffy, Amanda and Maimone, Mark and Yen, Jeng},
    year = {2015},
    pages = {1--10}
}
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TY - CONF
TI - Risk-aware Planetary Rover Operation: Autonomous Terrain Classification and Path Planning
AU - Ono, Masahiro
AU - Fuchs, Thomas J.
AU - Steffy, Amanda
AU - Maimone, Mark
AU - Yen, Jeng
AB - Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed ground-based Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers. The risk-aware rover operation tool is built upon two technologies. The first technology is a machine learning-based terrain classification that is capable of identifying potential hazards, such as pointy rocks and soft terrains, from images. The second technology is a risk-aware path planner based on rapidly-exploring random graph (RRG) and the A* search algorithms, which is capable of avoiding hazards identified by the terrain classifier with explicitly considering wheel placement. We demonstrate the integrated capability of the proposed risk-aware rover operation tool by using the images taken by the Curiosity rover.
C3 - Proceedings of the 36th IEEE Aerospace Conference
DA - 2015///
PY - 2015
DO - 10.1109/AERO.2015.7119022
SP - 1
EP - 10
UR - http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7119022&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7119022
ER -
Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed ground-based Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers. The risk-aware rover operation tool is built upon two technologies. The first technology is a machine learning-based terrain classification that is capable of identifying potential hazards, such as pointy rocks and soft terrains, from images. The second technology is a risk-aware path planner based on rapidly-exploring random graph (RRG) and the A* search algorithms, which is capable of avoiding hazards identified by the terrain classifier with explicitly considering wheel placement. We demonstrate the integrated capability of the proposed risk-aware rover operation tool by using the images taken by the Curiosity rover.
Robot-Centric Activity Prediction from First-Person Videos: What Will They Do to Me?
Michael S. Ryoo, Thomas J. Fuchs, Lu Xia, J. K. Aggarwal and Larry H. Matthies.
Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction, 2015
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{ryoo_robot-centric_2015,
    title = {Robot-{Centric} {Activity} {Prediction} from {First}-{Person} {Videos}: {What} {Will} {They} {Do} to {Me}?},
    url = {http://michaelryoo.com/papers/hri2015_ryoo.pdf},
    booktitle = {Proceedings of the 10th {ACM}/{IEEE} {International} {Conference} on {Human}-{Robot} {Interaction}},
    author = {Ryoo, Michael S. and Fuchs, Thomas J. and Xia, Lu and Aggarwal, J. K. and Matthies, Larry H.},
    year = {2015},
}
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TY - CONF
TI - Robot-Centric Activity Prediction from First-Person Videos: What Will They Do to Me?
AU - Ryoo, Michael S.
AU - Fuchs, Thomas J.
AU - Xia, Lu
AU - Aggarwal, J. K.
AU - Matthies, Larry H.
C3 - Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction
DA - 2015///
PY - 2015
UR - http://michaelryoo.com/papers/hri2015_ryoo.pdf
ER -
Crohn's Disease Segmentation from MRI Using Learned Image Priors.
D. Mahapatra, P. J. Schüffler, F. M. Vos and J. M. Buhmann.
Proceedings IEEE ISBI 2015, p. 625-628, 2015
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{mahapatra_crohns_2015,
    title = {Crohn's {Disease} {Segmentation} from {MRI} {Using} {Learned} {Image} {Priors}},
    url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951},
    abstract = {We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.},
    journal = {Proceedings IEEE ISBI 2015},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Vos, F. M. and Buhmann, J. M.},
    year = {2015},
    pages = {625--628}
}
Download Endnote/RIS citation
TY - JOUR
TI - Crohn's Disease Segmentation from MRI Using Learned Image Priors
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - Proceedings IEEE ISBI 2015
AB - We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
DA - 2015///
PY - 2015
SP - 625
EP - 628
UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951
ER -
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
Autonomous Real-time Detection of Plumes and Jets from Moons and Comets.
Kiri L. Wagstaff, David R. Thompson, Brian D. Bue and Thomas J. Fuchs.
ApJ, vol. 794, 1, p. 43, 2014
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{wagstaff_autonomous_2014,
    title = {Autonomous {Real}-time {Detection} of {Plumes} and {Jets} from {Moons} and {Comets}},
    volume = {794},
    issn = {0004-637X},
    url = {http://stacks.iop.org/0004-637X/794/i=1/a=43},
    doi = {10.1088/0004-637X/794/1/43},
    abstract = {Dynamic activity on the surface of distant moons, asteroids, and comets can manifest as jets or plumes. These phenomena provide information about the interior of the bodies and the forces (gravitation, radiation, thermal) they experience. Fast detection and follow-up study is imperative since the phenomena may be time-varying and because the observing window may be limited (e.g., during a flyby). We have developed an advanced method for real-time detection of plumes and jets using onboard analysis of the data as it is collected. In contrast to prior work, our technique is not restricted to plume detection from spherical bodies, making it relevant for irregularly shaped bodies such as comets. Further, our study analyzes raw data, the form in which it is available on board the spacecraft, rather than fully processed image products. In summary, we contribute a vital assessment of a technique that can be used on board tomorrow's deep space missions to detect, and respond quickly to, new occurrences of plumes and jets.},
    language = {en},
    number = {1},
    urldate = {2016-01-05TZ},
    journal = {The Astrophysical Journal},
    author = {Wagstaff, Kiri L. and Thompson, David R. and Bue, Brian D. and Fuchs, Thomas J.},
    year = {2014},
    pages = {43}
}
Download Endnote/RIS citation
TY - JOUR
TI - Autonomous Real-time Detection of Plumes and Jets from Moons and Comets
AU - Wagstaff, Kiri L.
AU - Thompson, David R.
AU - Bue, Brian D.
AU - Fuchs, Thomas J.
T2 - The Astrophysical Journal
AB - Dynamic activity on the surface of distant moons, asteroids, and comets can manifest as jets or plumes. These phenomena provide information about the interior of the bodies and the forces (gravitation, radiation, thermal) they experience. Fast detection and follow-up study is imperative since the phenomena may be time-varying and because the observing window may be limited (e.g., during a flyby). We have developed an advanced method for real-time detection of plumes and jets using onboard analysis of the data as it is collected. In contrast to prior work, our technique is not restricted to plume detection from spherical bodies, making it relevant for irregularly shaped bodies such as comets. Further, our study analyzes raw data, the form in which it is available on board the spacecraft, rather than fully processed image products. In summary, we contribute a vital assessment of a technique that can be used on board tomorrow's deep space missions to detect, and respond quickly to, new occurrences of plumes and jets.
DA - 2014///
PY - 2014
DO - 10.1088/0004-637X/794/1/43
DP - Institute of Physics
VL - 794
IS - 1
SP - 43
J2 - ApJ
LA - en
SN - 0004-637X
UR - http://stacks.iop.org/0004-637X/794/i=1/a=43
Y2 - 2016/01/05/T03:51:25Z
ER -
Dynamic activity on the surface of distant moons, asteroids, and comets can manifest as jets or plumes. These phenomena provide information about the interior of the bodies and the forces (gravitation, radiation, thermal) they experience. Fast detection and follow-up study is imperative since the phenomena may be time-varying and because the observing window may be limited (e.g., during a flyby). We have developed an advanced method for real-time detection of plumes and jets using onboard analysis of the data as it is collected. In contrast to prior work, our technique is not restricted to plume detection from spherical bodies, making it relevant for irregularly shaped bodies such as comets. Further, our study analyzes raw data, the form in which it is available on board the spacecraft, rather than fully processed image products. In summary, we contribute a vital assessment of a technique that can be used on board tomorrow's deep space missions to detect, and respond quickly to, new occurrences of plumes and jets.
Sparse Meta-Gaussian Information Bottleneck.
Melanie Rey, Thomas J. Fuchs and Volker Roth.
Proceedings of the 31st International Conference on Machine Learning, ICML, 2014
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@inproceedings{rey_sparse_2014,
    series = {{ICML}},
    title = {Sparse {Meta}-{Gaussian} {Information} {Bottleneck}},
    url = {http://jmlr.org/proceedings/papers/v32/rey14.pdf},
    booktitle = {Proceedings of the 31st {International} {Conference} on {Machine} {Learning}},
    author = {Rey, Melanie and Fuchs, Thomas J. and Roth, Volker},
    year = {2014},
}
Download Endnote/RIS citation
TY - CONF
TI - Sparse Meta-Gaussian Information Bottleneck
AU - Rey, Melanie
AU - Fuchs, Thomas J.
AU - Roth, Volker
T3 - ICML
C3 - Proceedings of the 31st International Conference on Machine Learning
DA - 2014///
PY - 2014
UR - http://jmlr.org/proceedings/papers/v32/rey14.pdf
ER -
KPNA2 Is Overexpressed in Human and Mouse Endometrial Cancers and Promotes Cellular Proliferation.
Kristian Ikenberg, Nadejda Valtcheva, Simone Brandt, Qing Zhong, Christine E. Wong, Aurelia Noske, Markus Rechsteiner, Jan H. Rueschoff, Rosemarie Caduff, Athanassios Dellas, Ellen Obermann, Daniel Fink, Thomas J. Fuchs, Wilhelm Krek, Holger Moch, Ian J. Frew and Peter J. Wild.
The Journal of Pathology, vol. 234, 2, p. 239–252, 2014
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@article{ikenberg_kpna2_2014,
    title = {{KPNA}2 {Is} {Overexpressed} in {Human} and {Mouse} {Endometrial} {Cancers} and {Promotes} {Cellular} {Proliferation}},
    volume = {234},
    issn = {1096-9896},
    url = {http://dx.doi.org/10.1002/path.4390},
    doi = {10.1002/path.4390},
    number = {2},
    journal = {The Journal of Pathology},
    author = {Ikenberg, Kristian and Valtcheva, Nadejda and Brandt, Simone and Zhong, Qing and Wong, Christine E. and Noske, Aurelia and Rechsteiner, Markus and Rueschoff, Jan H. and Caduff, Rosemarie and Dellas, Athanassios and Obermann, Ellen and Fink, Daniel and Fuchs, Thomas J. and Krek, Wilhelm and Moch, Holger and Frew, Ian J. and Wild, Peter J.},
    year = {2014},
    keywords = {EMT, KPNA2, Snail, biomarker, endometrial cancer, importin},
    pages = {239--252}
}
Download Endnote/RIS citation
TY - JOUR
TI - KPNA2 Is Overexpressed in Human and Mouse Endometrial Cancers and Promotes Cellular Proliferation
AU - Ikenberg, Kristian
AU - Valtcheva, Nadejda
AU - Brandt, Simone
AU - Zhong, Qing
AU - Wong, Christine E.
AU - Noske, Aurelia
AU - Rechsteiner, Markus
AU - Rueschoff, Jan H.
AU - Caduff, Rosemarie
AU - Dellas, Athanassios
AU - Obermann, Ellen
AU - Fink, Daniel
AU - Fuchs, Thomas J.
AU - Krek, Wilhelm
AU - Moch, Holger
AU - Frew, Ian J.
AU - Wild, Peter J.
T2 - The Journal of Pathology
DA - 2014///
PY - 2014
DO - 10.1002/path.4390
VL - 234
IS - 2
SP - 239
EP - 252
SN - 1096-9896
UR - http://dx.doi.org/10.1002/path.4390
KW - EMT
KW - KPNA2
KW - Snail
KW - biomarker
KW - endometrial cancer
KW - importin
ER -
Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry.
C. Giesen, H. A. Wang, D. Schapiro, N. Zivanovic, A. Jacobs, B. Hattendorf, P. J. Schüffler, D. Grolimund, J. M. Buhmann, S. Brandt, Z. Varga, P. J. Wild, D. Gunther and B. Bodenmiller.
Nature methods, vol. 11, p. 417-22, 2014
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@article{giesen_highly_2014,
    title = {Highly {Multiplexed} {Imaging} of {Tumor} {Tissues} with {Subcellular} {Resolution} by {Mass} {Cytometry}},
    volume = {11},
    issn = {1548-7105 (Electronic) 1548-7091 (Linking)},
    url = {http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html},
    doi = {10.1038/nmeth.2869},
    abstract = {Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.},
    journal = {Nat Methods},
    author = {Giesen, C. and Wang, H. A. and Schapiro, D. and Zivanovic, N. and Jacobs, A. and Hattendorf, B. and Sch\"uffler, P. J. and Grolimund, D. and Buhmann, J. M. and Brandt, S. and Varga, Z. and Wild, P. J. and Gunther, D. and Bodenmiller, B.},
    year = {2014},
    pages = {417--22}
}
Download Endnote/RIS citation
TY - JOUR
TI - Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry
AU - Giesen, C.
AU - Wang, H. A.
AU - Schapiro, D.
AU - Zivanovic, N.
AU - Jacobs, A.
AU - Hattendorf, B.
AU - Schüffler, P. J.
AU - Grolimund, D.
AU - Buhmann, J. M.
AU - Brandt, S.
AU - Varga, Z.
AU - Wild, P. J.
AU - Gunther, D.
AU - Bodenmiller, B.
T2 - Nat Methods
AB - Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
DA - 2014///
PY - 2014
DO - 10.1038/nmeth.2869
VL - 11
SP - 417
EP - 22
J2 - Nature methods
SN - 1548-7105 (Electronic) 1548-7091 (Linking)
UR - http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html
ER -
Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys.
S.G. Djorgovski, A. Mahabal, C. Donalek, M. Graham, A. Drake, M. Turmon and T.J. Fuchs.
2014 IEEE 10th International Conference on e-Science (e-Science), 2014 IEEE 10th International Conference on e-Science (e-Science), vol. 1, p. 204-211, 2014
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@inproceedings{djorgovski_automated_2014,
    title = {Automated {Real}-{Time} {Classification} and {Decision} {Making} in {Massive} {Data} {Streams} from {Synoptic} {Sky} {Surveys}},
    volume = {1},
    url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6972266&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6972266},
    doi = {10.1109/eScience.2014.7},
    abstract = {The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.},
    booktitle = {2014 {IEEE} 10th {International} {Conference} on e-{Science} (e-{Science})},
    author = {Djorgovski, S.G. and Mahabal, A. and Donalek, C. and Graham, M. and Drake, A. and Turmon, M. and Fuchs, T.J.},
    year = {2014},
    keywords = {Astronomy, Automated decision making, Bayesian methods, CRTS, Catalina Real-time Transient Survey, Cathode ray tubes, Data analysis, Extraterrestrial measurements, Massive data streams, Pollution measurement, Real-time systems, Sky surveys, Time measurement, Transient analysis, astronomical surveys, astronomy applications, astronomy computing, automated real-time classification, automated real-time decision making, black hole formation, classification, cosmic explosions, decision making, digital synoptic sky surveys, gamma ray bursts, jets, learning (artificial intelligence), machine learning, machine learning tools, pattern classification, potentially hazardous asteroids, relativistic phenomena, scientific data collection, supernovae, technological data collection},
    pages = {204--211}
}
Download Endnote/RIS citation
TY - CONF
TI - Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys
AU - Djorgovski, S.G.
AU - Mahabal, A.
AU - Donalek, C.
AU - Graham, M.
AU - Drake, A.
AU - Turmon, M.
AU - Fuchs, T.J.
T2 - 2014 IEEE 10th International Conference on e-Science (e-Science)
AB - The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
C3 - 2014 IEEE 10th International Conference on e-Science (e-Science)
DA - 2014///
PY - 2014
DO - 10.1109/eScience.2014.7
DP - IEEE Xplore
VL - 1
SP - 204
EP - 211
UR - http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6972266&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6972266
KW - Astronomy
KW - Automated decision making
KW - Bayesian methods
KW - CRTS
KW - Catalina Real-time Transient Survey
KW - Cathode ray tubes
KW - Data analysis
KW - Extraterrestrial measurements
KW - Massive data streams
KW - Pollution measurement
KW - Real-time systems
KW - Sky surveys
KW - Time measurement
KW - Transient analysis
KW - astronomical surveys
KW - astronomy applications
KW - astronomy computing
KW - automated real-time classification
KW - automated real-time decision making
KW - black hole formation
KW - classification
KW - cosmic explosions
KW - decision making
KW - digital synoptic sky surveys
KW - gamma ray bursts
KW - jets
KW - learning (artificial intelligence)
KW - machine learning
KW - machine learning tools
KW - pattern classification
KW - potentially hazardous asteroids
KW - relativistic phenomena
KW - scientific data collection
KW - supernovae
KW - technological data collection
ER -
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
Early Recognition of Human Activities from First-Person Videos Using Onset Representations.
M. S. Ryoo, Thomas J. Fuchs, Lu Xia, J. K. Aggarwal and Larry Matthies.
arXiv:1406.5309 [cs], 2014
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{ryoo_early_2014,
    title = {Early {Recognition} of {Human} {Activities} from {First}-{Person} {Videos} {Using} {Onset} {Representations}},
    url = {http://arxiv.org/abs/1406.5309},
    abstract = {In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos.},
    urldate = {2015-12-23TZ},
    journal = {arXiv:1406.5309 [cs]},
    author = {Ryoo, M. S. and Fuchs, Thomas J. and Xia, Lu and Aggarwal, J. K. and Matthies, Larry},
    year = {2014},
    keywords = {Computer Science - Computer Vision and Pattern Recognition}
}
Download Endnote/RIS citation
TY - JOUR
TI - Early Recognition of Human Activities from First-Person Videos Using Onset Representations
AU - Ryoo, M. S.
AU - Fuchs, Thomas J.
AU - Xia, Lu
AU - Aggarwal, J. K.
AU - Matthies, Larry
T2 - arXiv:1406.5309 [cs]
AB - In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos.
DA - 2014///
PY - 2014
DP - arXiv.org
UR - http://arxiv.org/abs/1406.5309
Y2 - 2015/12/23/T16:01:25Z
KW - Computer Science - Computer Vision and Pattern Recognition
ER -
In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos.
Computer Aided Crohn's Disease Severity Assessment in MRI.
Peter J. Schüffler, Dwarikanath Mahapatra, Franciscus M. Vos and Joachim M. Buhmann.
VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook, 2014
Best Poster Award
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@misc{schuffler_computer_2014,
    address = {London},
    type = {Poster},
    title = {Computer {Aided} {Crohn}'s {Disease} {Severity} {Assessment} in {MRI}},
    url = {https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Vos, Franciscus M. and Buhmann, Joachim M.},
    year = {2014},
}
Download Endnote/RIS citation
TY - SLIDE
TI - Computer Aided Crohn's Disease Severity Assessment in MRI
T2 - VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook
A2 - Schüffler, Peter J.
A2 - Mahapatra, Dwarikanath
A2 - Vos, Franciscus M.
A2 - Buhmann, Joachim M.
CY - London
DA - 2014///
PY - 2014
M3 - Poster
UR - https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI
ER -
Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging.
P. J. Schüffler, D. Mahapatra, R. E. Naziroglu, Z. Li, C. A. J. Puylaert, R. Andriantsimiavona, F. M. Vos, D. A. Pendsé, C. Yung Nio, J. Stoker, S. A. Taylor and J. M. Buhmann.
6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications, 2014
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schuffler_semi-automatic_2014,
    title = {Semi-{Automatic} {Crohn}'s {Disease} {Severity} {Estimation} on {MR} {Imaging}},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12},
    abstract = {Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).},
    author = {Sch\"uffler, P. J. and Mahapatra, D. and Naziroglu, R. E. and Li, Z. and Puylaert, C. A. J. and Andriantsimiavona, R. and Vos, F. M. and Pends\'e, D. A. and Nio, C. Yung and Stoker, J. and Taylor, S. A. and Buhmann, J. M.},
    year = {2014},
}
Download Endnote/RIS citation
TY - CONF
TI - Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging
AU - Schüffler, P. J.
AU - Mahapatra, D.
AU - Naziroglu, R. E.
AU - Li, Z.
AU - Puylaert, C. A. J.
AU - Andriantsimiavona, R.
AU - Vos, F. M.
AU - Pendsé, D. A.
AU - Nio, C. Yung
AU - Stoker, J.
AU - Taylor, S. A.
AU - Buhmann, J. M.
T2 - 6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications
AB - Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
DA - 2014///
PY - 2014
UR - http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12
ER -
Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
Autonomous Onboard Surface Feature Detection for Flyby Missions.
Thomas J. Fuchs, Brian D. Bue, Julie Castillo-Rogez, Steve A. Chien, Kiri Wagstaff and David R. Thompson.
Proceedings of the 12th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), 2014
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@inproceedings{fuchs_autonomous_2014,
    title = {Autonomous {Onboard} {Surface} {Feature} {Detection} for {Flyby} {Missions}},
    booktitle = {Proceedings of the 12th {International} {Symposium} on {Artificial} {Intelligence}, {Robotics} and {Automation} in {Space} (i-{SAIRAS})},
    author = {Fuchs, Thomas J. and Bue, Brian D. and Castillo-Rogez, Julie and Chien, Steve A. and Wagstaff, Kiri and Thompson, David R.},
    year = {2014},
}
Download Endnote/RIS citation
TY - CONF
TI - Autonomous Onboard Surface Feature Detection for Flyby Missions
AU - Fuchs, Thomas J.
AU - Bue, Brian D.
AU - Castillo-Rogez, Julie
AU - Chien, Steve A.
AU - Wagstaff, Kiri
AU - Thompson, David R.
C3 - Proceedings of the 12th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS)
DA - 2014///
PY - 2014
ER -
TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation.
Peter J. Schüffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
Journal of Pathology Informatics, vol. 4, 2, p. 2, 2013
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@article{schuffler_tmarker:_2013,
    title = {{TMARKER}: {A} {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Staining} {Estimation}},
    volume = {4},
    url = {http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=2;epage=2;aulast=Sch%FCffler;t=6},
    doi = {10.4103/2153-3539.109804},
    abstract = {Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.},
    number = {2},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2013},
    pages = {2}
}
Download Endnote/RIS citation
TY - JOUR
TI - TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - Journal of Pathology Informatics
AB - Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
DA - 2013///
PY - 2013
DO - 10.4103/2153-3539.109804
VL - 4
IS - 2
SP - 2
UR - http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=2;epage=2;aulast=Sch%FCffler;t=6
ER -
Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
Smart, Texture-Sensitive Instrument Classification for in Situ Rock and Layer Analysis.
K. L. Wagstaff, D. R. Thompson, W. Abbey, A. Allwood, D. Bekker, N. A. Cabrol, Thomas J. Fuchs and K. Ortega.
Geophysical Research Letters, vol. 40, 16, p. 4188–4193, 2013
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@article{wagstaff_smart_2013,
    title = {Smart, {Texture}-{Sensitive} {Instrument} {Classification} for in {Situ} {Rock} and {Layer} {Analysis}},
    volume = {40},
    issn = {1944-8007},
    url = {http://dx.doi.org/10.1002/grl.50817},
    doi = {10.1002/grl.50817},
    number = {16},
    journal = {Geophysical Research Letters},
    author = {Wagstaff, K. L. and Thompson, D. R. and Abbey, W. and Allwood, A. and Bekker, D. and Cabrol, N. A. and Fuchs, Thomas J. and Ortega, K.},
    year = {2013},
    pages = {4188--4193}
}
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TY - JOUR
TI - Smart, Texture-Sensitive Instrument Classification for in Situ Rock and Layer Analysis
AU - Wagstaff, K. L.
AU - Thompson, D. R.
AU - Abbey, W.
AU - Allwood, A.
AU - Bekker, D.
AU - Cabrol, N. A.
AU - Fuchs, Thomas J.
AU - Ortega, K.
T2 - Geophysical Research Letters
DA - 2013///
PY - 2013
DO - 10.1002/grl.50817
VL - 40
IS - 16
SP - 4188
EP - 4193
SN - 1944-8007
UR - http://dx.doi.org/10.1002/grl.50817
ER -
TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations.
Peter J. Schueffler, Niels Rupp, Cheng S. Ong, Joachim M. Buhmann, Thomas J. Fuchs and Peter J. Wild.
German Society of Pathology 97th Annual Meeting, 2013
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schueffler_tmarker:_2013,
    title = {{TMARKER}: {A} {Robust} and {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Immunohistochemical} {Staining} {Estimations}},
    url = {http://link.springer.com/article/10.1007%2Fs00292-013-1765-2},
    abstract = {Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
    Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
    Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
    Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.},
    booktitle = {German {Society} of {Pathology} 97th {Annual} {Meeting}},
    author = {Schueffler, Peter J. and Rupp, Niels and Ong, Cheng S. and Buhmann, Joachim M. and Fuchs, Thomas J. and Wild, Peter J.},
    year = {2013},
}
Download Endnote/RIS citation
TY - CONF
TI - TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations
AU - Schueffler, Peter J.
AU - Rupp, Niels
AU - Ong, Cheng S.
AU - Buhmann, Joachim M.
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AB - Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
C3 - German Society of Pathology 97th Annual Meeting
DA - 2013///
PY - 2013
UR - http://link.springer.com/article/10.1007%2Fs00292-013-1765-2
ER -
Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming. Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision. Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas. Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma.
Peter J. Schueffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann.
In: Similarity-Based Pattern Analysis and Recognition, p. 219–246, Advances in Computer Vision and Pattern Recognition, Springer, ISBN 978-1-4471-5627-7, 2013
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@incollection{schueffler_automated_2013,
    address = {London},
    series = {Advances in {Computer} {Vision} and {Pattern} {Recognition}},
    title = {Automated {Analysis} of {Tissue} {Micro}-{Array} {Images} on the {Example} of {Renal} {Cell} {Carcinoma}},
    isbn = {978-1-4471-5627-7},
    url = {https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7},
    abstract = {Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.},
    booktitle = {Similarity-{Based} {Pattern} {Analysis} and {Recognition}},
    publisher = {Springer},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2013},
    pages = {219--246}
}
Download Endnote/RIS citation
TY - CHAP
TI - Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng Soon
AU - Roth, Volker
AU - Buhmann, Joachim M.
T2 - Similarity-Based Pattern Analysis and Recognition
T3 - Advances in Computer Vision and Pattern Recognition
AB - Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.
CY - London
DA - 2013///
PY - 2013
SP - 219
EP - 246
PB - Springer
SN - 978-1-4471-5627-7
UR - https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7
ER -
Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.
A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images.
Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos, Jesica Makanyanga, Doug A. Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor and Joachim M. Buhmann.
In: Hiroyuki Yoshida, Simon Warfield and Michael Vannier (eds.) Abdominal Imaging. Computation and Clinical Applications, vol. 8198, p. 1-10, Lecture Notes in Computer Science, Springer Berlin Heidelberg, ISBN 978-3-642-41082-6, 2013
Outstanding Paper Award
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@incollection{schuffler_model_2013,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {A {Model} {Development} {Pipeline} for {Crohn}'s {Disease} {Severity} {Assessment} from {Magnetic} {Resonance} {Images}},
    volume = {8198},
    isbn = {978-3-642-41082-6},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1},
    abstract = {Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.},
    booktitle = {Abdominal {Imaging}. {Computation} and {Clinical} {Applications}},
    publisher = {Springer Berlin Heidelberg},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Tielbeek, Jeroen A. W. and Vos, Franciscus M. and Makanyanga, Jesica and Pends\'e, Doug A. and Nio, C. Yung and Stoker, Jaap and Taylor, Stuart A. and Buhmann, Joachim M.},
    editor = {Yoshida, Hiroyuki and Warfield, Simon and Vannier, Michael},
    year = {2013},
    keywords = {AIS, CDEIS, Crohn’s Disease, MaRIA, abdominal MRI},
    pages = {1--10}
}
Download Endnote/RIS citation
TY - CHAP
TI - A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images
AU - Schüffler, Peter J.
AU - Mahapatra, Dwarikanath
AU - Tielbeek, Jeroen A. W.
AU - Vos, Franciscus M.
AU - Makanyanga, Jesica
AU - Pendsé, Doug A.
AU - Nio, C. Yung
AU - Stoker, Jaap
AU - Taylor, Stuart A.
AU - Buhmann, Joachim M.
T2 - Abdominal Imaging. Computation and Clinical Applications
A2 - Yoshida, Hiroyuki
A2 - Warfield, Simon
A2 - Vannier, Michael
T3 - Lecture Notes in Computer Science
AB - Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
DA - 2013///
PY - 2013
VL - 8198
SP - 1
EP - 10
PB - Springer Berlin Heidelberg
SN - 978-3-642-41082-6
UR - http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1
KW - AIS
KW - CDEIS
KW - Crohn’s Disease
KW - MaRIA
KW - abdominal MRI
ER -
Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets.
Ciro Donalek, Arun Kumar A., S. G. Djorgovski, Ashish A. Mahabal, Matthew J. Graham, Thomas J. Fuchs, Michael J. Turmon, N. Sajeeth Philip, Michael Ting-Chang Yang and Giuseppe Longo.
IEEE International Conference on Big Data, 2013
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@article{donalek_feature_2013,
    title = {Feature {Selection} {Strategies} for {Classifying} {High} {Dimensional} {Astronomical} {Data} {Sets}},
    url = {http://arxiv.org/abs/1310.1976},
    abstract = {The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.},
    urldate = {2016-01-05TZ},
    journal = {IEEE International Conference on Big Data},
    author = {Donalek, Ciro and A., Arun Kumar and Djorgovski, S. G. and Mahabal, Ashish A. and Graham, Matthew J. and Fuchs, Thomas J. and Turmon, Michael J. and Philip, N. Sajeeth and Yang, Michael Ting-Chang and Longo, Giuseppe},
    year = {2013},
    keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computer Vision and Pattern Recognition}
}
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TY - JOUR
TI - Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets
AU - Donalek, Ciro
AU - A., Arun Kumar
AU - Djorgovski, S. G.
AU - Mahabal, Ashish A.
AU - Graham, Matthew J.
AU - Fuchs, Thomas J.
AU - Turmon, Michael J.
AU - Philip, N. Sajeeth
AU - Yang, Michael Ting-Chang
AU - Longo, Giuseppe
T2 - IEEE International Conference on Big Data
AB - The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.
DA - 2013///
PY - 2013
DP - arXiv.org
UR - http://arxiv.org/abs/1310.1976
Y2 - 2016/01/05/T04:03:47Z
KW - Astrophysics - Instrumentation and Methods for Astrophysics
KW - Computer Science - Computer Vision and Pattern Recognition
ER -
The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.
Quickly Boosting Decision Trees – Pruning Underachieving Features using a Provable Bound.
Ron Appel, Piotr Dollar, Thomas J. Fuchs and Pietro Perona.
Proceedings of the 30th International Conference on Machine Learning (ICML), vol. 28, p. 594-602, 2013
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@inproceedings{appel_quickly_2013,
    title = {Quickly {Boosting} {Decision} {Trees} – {Pruning} {Underachieving} {Features} using a {Provable} {Bound}},
    volume = {28},
    url = {http://jmlr.org/proceedings/papers/v28/appel13.html},
    booktitle = {Proceedings of the 30th {International} {Conference} on {Machine} {Learning} ({ICML})},
    author = {Appel, Ron and Dollar, Piotr and Fuchs, Thomas J. and Perona, Pietro},
    year = {2013},
    pages = {594--602}
}
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TY - CONF
TI - Quickly Boosting Decision Trees – Pruning Underachieving Features using a Provable Bound
AU - Appel, Ron
AU - Dollar, Piotr
AU - Fuchs, Thomas J.
AU - Perona, Pietro
C3 - Proceedings of the 30th International Conference on Machine Learning (ICML)
DA - 2013///
PY - 2013
VL - 28
SP - 594
EP - 602
UR - http://jmlr.org/proceedings/papers/v28/appel13.html
ER -
Recognizing Humans in Motion: Trajectory-based Areal Video Analysis.
Yumi Iwashita, Michael Ryoo, Thomas J. Fuchs and Curtis Padgett.
24th British Machine Vision Conference (BMVC), 2013
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@inproceedings{iwashita_recognizing_2013,
    title = {Recognizing {Humans} in {Motion}: {Trajectory}-based {Areal} {Video} {Analysis}},
    booktitle = {24th {British} {Machine} {Vision} {Conference} ({BMVC})},
    author = {Iwashita, Yumi and Ryoo, Michael and Fuchs, Thomas J. and Padgett, Curtis},
    year = {2013},
}
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TY - CONF
TI - Recognizing Humans in Motion: Trajectory-based Areal Video Analysis
AU - Iwashita, Yumi
AU - Ryoo, Michael
AU - Fuchs, Thomas J.
AU - Padgett, Curtis
C3 - 24th British Machine Vision Conference (BMVC)
DA - 2013///
PY - 2013
ER -
Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI.
D. Mahapatra, P. J. Schüffler, J. A. W. Tielbeek, J. C. Makanyanga, J. Stoker, S. A. Taylor, F. M. Vos and J. M. Buhmann.
IEEE Transactions on Medical Imaging, vol. 32, p. 2332-2347, 2013
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@article{mahapatra_automatic_2013,
    title = {Automatic {Detection} and {Segmentation} of {Crohn}'s {Disease} {Tissues} from {Abdominal} {MRI}},
    volume = {32},
    issn = {0278-0062},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949},
    doi = {10.1109/TMI.2013.2282124},
    abstract = {We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.},
    journal = {IEEE Transactions on Medical Imaging},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Tielbeek, J. A. W. and Makanyanga, J. C. and Stoker, J. and Taylor, S. A. and Vos, F. M. and Buhmann, J. M.},
    year = {2013},
    keywords = {Anisotropic magnetoresistance, Context, Crohn\&\#x2019, Diseases, Entropy, Image segmentation, Radio frequency, content, graph cut, image features, probability maps, random forests, s disease, segmentation, semantic information, shape, supervoxels},
    pages = {2332--2347}
}
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TY - JOUR
TI - Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Tielbeek, J. A. W.
AU - Makanyanga, J. C.
AU - Stoker, J.
AU - Taylor, S. A.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - IEEE Transactions on Medical Imaging
AB - We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
DA - 2013///
PY - 2013
DO - 10.1109/TMI.2013.2282124
VL - 32
SP - 2332
EP - 2347
SN - 0278-0062
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949
KW - Anisotropic magnetoresistance
KW - Context
KW - Crohn’
KW - Diseases
KW - Entropy
KW - Image segmentation
KW - Radio frequency
KW - content
KW - graph cut
KW - image features
KW - probability maps
KW - random forests
KW - s disease
KW - segmentation
KW - semantic information
KW - shape
KW - supervoxels
ER -
We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
TextureCam: A Smart Camera for Microscale, Mesoscale, and Deep Space.
William Abbey, Abigail Allwood, Dmitriy Bekker, Benjamin Bornstein, Nathalie A. Cabrol, Rebecca Castano, Steve A. Chien, Joshua Doubleday, Tara Estlin, Greydon Foil, Thomas J. Fuchs, Daniel Howarth, Kevin Ortega, David R. Thompson and Kiri L. Wagstaff.
44th Lunar and Planetary Science Conference, p. 2209, 2013
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@inproceedings{abbey_texturecam:_2013,
    title = {{TextureCam}: {A} {Smart} {Camera} for {Microscale}, {Mesoscale}, and {Deep} {Space}},
    booktitle = {44th {Lunar} and {Planetary} {Science} {Conference}},
    author = {Abbey, William and Allwood, Abigail and Bekker, Dmitriy and Bornstein, Benjamin and Cabrol, Nathalie A. and Castano, Rebecca and Chien, Steve A. and Doubleday, Joshua and Estlin, Tara and Foil, Greydon and Fuchs, Thomas J. and Howarth, Daniel and Ortega, Kevin and Thompson, David R. and Wagstaff, Kiri L.},
    year = {2013},
    pages = {2209}
}
Download Endnote/RIS citation
TY - CONF
TI - TextureCam: A Smart Camera for Microscale, Mesoscale, and Deep Space
AU - Abbey, William
AU - Allwood, Abigail
AU - Bekker, Dmitriy
AU - Bornstein, Benjamin
AU - Cabrol, Nathalie A.
AU - Castano, Rebecca
AU - Chien, Steve A.
AU - Doubleday, Joshua
AU - Estlin, Tara
AU - Foil, Greydon
AU - Fuchs, Thomas J.
AU - Howarth, Daniel
AU - Ortega, Kevin
AU - Thompson, David R.
AU - Wagstaff, Kiri L.
C3 - 44th Lunar and Planetary Science Conference
DA - 2013///
PY - 2013
SP - 2209
ER -
Structure Preserving Embedding of Dissimilarity Data.
Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran and Joachim M. Buhmann.
In: Similarity-Based Pattern Analysis and Recognition, p. 157–178, Advances in Computer Vision and Pattern Recognition, Springer, ISBN 978-1-4471-5627-7, 2013
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@incollection{roth_structure_2013,
    series = {Advances in {Computer} {Vision} and {Pattern} {Recognition}},
    title = {Structure {Preserving} {Embedding} of {Dissimilarity} {Data}},
    isbn = {978-1-4471-5627-7},
    url = {https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7},
    booktitle = {Similarity-{Based} {Pattern} {Analysis} and {Recognition}},
    publisher = {Springer},
    author = {Roth, Volker and Fuchs, Thomas J. and Vogt, Julia E. and Prabhakaran, Sandhya and Buhmann, Joachim M.},
    year = {2013},
    pages = {157--178}
}
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TY - CHAP
TI - Structure Preserving Embedding of Dissimilarity Data
AU - Roth, Volker
AU - Fuchs, Thomas J.
AU - Vogt, Julia E.
AU - Prabhakaran, Sandhya
AU - Buhmann, Joachim M.
T2 - Similarity-Based Pattern Analysis and Recognition
T3 - Advances in Computer Vision and Pattern Recognition
DA - 2013///
PY - 2013
SP - 157
EP - 178
PB - Springer
SN - 978-1-4471-5627-7
UR - https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7
ER -
p53 Suppresses Type II Endometrial Carcinomas in Mice and Governs Endometrial Tumour Aggressiveness in Humans.
Peter J. Wild, Kristian Ikenberg, Thomas J. Fuchs, Markus Rechsteiner, Strahil Georgiev, Niklaus Fankhauser, Aurelia Noske, Matthias Roessle, Rosmarie Caduff, Athanassios Dellas, Daniel Fink, Holger Moch, Wilhelm Krek and Ian J. Frew.
EMBO Molecular Medicine, vol. 4, 8, p. 808-824, 2012
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@article{wild_p53_2012,
    title = {p53 {Suppresses} {Type} {II} {Endometrial} {Carcinomas} in {Mice} and {Governs} {Endometrial} {Tumour} {Aggressiveness} in {Humans}},
    volume = {4},
    issn = {17574676},
    shorttitle = {p53 suppresses type {II} endometrial carcinomas in mice and governs endometrial tumour aggressiveness in humans},
    url = {http://embomolmed.embopress.org/cgi/doi/10.1002/emmm.201101063},
    doi = {10.1002/emmm.201101063},
    abstract = {Type II endometrial carcinomas are a highly aggressive group of tumour subtypes that are frequently associated with inactivation of the TP53 tumour suppressor gene. We show that mice with endometrium-specific deletion of Trp53 initially exhibited histological changes that are identical to known precursor lesions of type II endometrial carcinomas in humans and later developed carcinomas representing all type II subtypes. The mTORC1 signalling pathway was frequently activated in these precursor lesions and tumours, suggesting a genetic cooperation between this pathway and Trp53 deficiency in tumour initiation. Consistent with this idea, analyses of 521 human endometrial carcinomas identified frequent mTORC1 pathway activation in type I as well as type II endometrial carcinoma subtypes. mTORC1 pathway activation and p53 expression or mutation status each independently predicted poor patient survival. We suggest that molecular alterations in p53 and the mTORC1 pathway play different roles in the initiation of the different endometrial cancer subtypes, but that combined p53 inactivation and mTORC1 pathway activation are unifying pathogenic features among histologically diverse subtypes of late stage aggressive endometrial tumours.},
    language = {en},
    number = {8},
    urldate = {2016-04-12TZ},
    journal = {EMBO Molecular Medicine},
    author = {Wild, Peter J. and Ikenberg, Kristian and Fuchs, Thomas J. and Rechsteiner, Markus and Georgiev, Strahil and Fankhauser, Niklaus and Noske, Aurelia and Roessle, Matthias and Caduff, Rosmarie and Dellas, Athanassios and Fink, Daniel and Moch, Holger and Krek, Wilhelm and Frew, Ian J.},
    year = {2012},
    pages = {808--824}
}
Download Endnote/RIS citation
TY - JOUR
TI - p53 Suppresses Type II Endometrial Carcinomas in Mice and Governs Endometrial Tumour Aggressiveness in Humans
AU - Wild, Peter J.
AU - Ikenberg, Kristian
AU - Fuchs, Thomas J.
AU - Rechsteiner, Markus
AU - Georgiev, Strahil
AU - Fankhauser, Niklaus
AU - Noske, Aurelia
AU - Roessle, Matthias
AU - Caduff, Rosmarie
AU - Dellas, Athanassios
AU - Fink, Daniel
AU - Moch, Holger
AU - Krek, Wilhelm
AU - Frew, Ian J.
T2 - EMBO Molecular Medicine
AB - Type II endometrial carcinomas are a highly aggressive group of tumour subtypes that are frequently associated with inactivation of the TP53 tumour suppressor gene. We show that mice with endometrium-specific deletion of Trp53 initially exhibited histological changes that are identical to known precursor lesions of type II endometrial carcinomas in humans and later developed carcinomas representing all type II subtypes. The mTORC1 signalling pathway was frequently activated in these precursor lesions and tumours, suggesting a genetic cooperation between this pathway and Trp53 deficiency in tumour initiation. Consistent with this idea, analyses of 521 human endometrial carcinomas identified frequent mTORC1 pathway activation in type I as well as type II endometrial carcinoma subtypes. mTORC1 pathway activation and p53 expression or mutation status each independently predicted poor patient survival. We suggest that molecular alterations in p53 and the mTORC1 pathway play different roles in the initiation of the different endometrial cancer subtypes, but that combined p53 inactivation and mTORC1 pathway activation are unifying pathogenic features among histologically diverse subtypes of late stage aggressive endometrial tumours.
DA - 2012///
PY - 2012
DO - 10.1002/emmm.201101063
DP - CrossRef
VL - 4
IS - 8
SP - 808
EP - 824
LA - en
SN - 17574676
ST - p53 suppresses type II endometrial carcinomas in mice and governs endometrial tumour aggressiveness in humans
UR - http://embomolmed.embopress.org/cgi/doi/10.1002/emmm.201101063
Y2 - 2016/04/12/T13:58:18Z
ER -
Type II endometrial carcinomas are a highly aggressive group of tumour subtypes that are frequently associated with inactivation of the TP53 tumour suppressor gene. We show that mice with endometrium-specific deletion of Trp53 initially exhibited histological changes that are identical to known precursor lesions of type II endometrial carcinomas in humans and later developed carcinomas representing all type II subtypes. The mTORC1 signalling pathway was frequently activated in these precursor lesions and tumours, suggesting a genetic cooperation between this pathway and Trp53 deficiency in tumour initiation. Consistent with this idea, analyses of 521 human endometrial carcinomas identified frequent mTORC1 pathway activation in type I as well as type II endometrial carcinoma subtypes. mTORC1 pathway activation and p53 expression or mutation status each independently predicted poor patient survival. We suggest that molecular alterations in p53 and the mTORC1 pathway play different roles in the initiation of the different endometrial cancer subtypes, but that combined p53 inactivation and mTORC1 pathway activation are unifying pathogenic features among histologically diverse subtypes of late stage aggressive endometrial tumours.
End-to-End Dexterous Manipulation with Deliberate Interactive Estimation.
Nicolas H. Hudson, Tom Howard, Jeremy Ma, Abhinandan Jain, Max Bajracharya, Steven Myint, Larry Matthies, Paul Backes, Paul Hebert, Thomas J. Fuchs and Joel Burdick.
IEEE International Conference on Robotics and Automation (ICRA), p. 2371-2378, 2012
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@inproceedings{hudson_end--end_2012,
    title = {End-to-{End} {Dexterous} {Manipulation} with {Deliberate} {Interactive} {Estimation}},
    url = {http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?reload=true&arnumber=6225101&contentType=Conference+Publications},
    doi = {10.1109/ICRA.2012.6225101},
    abstract = {This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.},
    booktitle = {{IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
    author = {Hudson, Nicolas H. and Howard, Tom and Ma, Jeremy and Jain, Abhinandan and Bajracharya, Max and Myint, Steven and Matthies, Larry and Backes, Paul and Hebert, Paul and Fuchs, Thomas J. and Burdick, Joel},
    year = {2012},
    pages = {2371--2378}
}
Download Endnote/RIS citation
TY - CONF
TI - End-to-End Dexterous Manipulation with Deliberate Interactive Estimation
AU - Hudson, Nicolas H.
AU - Howard, Tom
AU - Ma, Jeremy
AU - Jain, Abhinandan
AU - Bajracharya, Max
AU - Myint, Steven
AU - Matthies, Larry
AU - Backes, Paul
AU - Hebert, Paul
AU - Fuchs, Thomas J.
AU - Burdick, Joel
AB - This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.
C3 - IEEE International Conference on Robotics and Automation (ICRA)
DA - 2012///
PY - 2012
DO - 10.1109/ICRA.2012.6225101
SP - 2371
EP - 2378
UR - http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?reload=true&arnumber=6225101&contentType=Conference+Publications
ER -
This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.
Smart Cameras for Remote Science Survey.
David R. Thompson, William Abbey, Abigail Allwood, Dmitriy Bekker, Benjamin Bornstein, Nathalie A. Cabrol, Rebecca Castano, Tara Estlin, Thomas J. Fuchs and Kiri L. Wagstaff.
Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), 2012
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@inproceedings{thompson_smart_2012,
    title = {Smart {Cameras} for {Remote} {Science} {Survey}},
    url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.5807},
    abstract = {Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels- distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.},
    booktitle = {Proceedings of the 10th {International} {Symposium} on {Artificial} {Intelligence}, {Robotics} and {Automation} in {Space} (i-{SAIRAS})},
    author = {Thompson, David R. and Abbey, William and Allwood, Abigail and Bekker, Dmitriy and Bornstein, Benjamin and Cabrol, Nathalie A. and Castano, Rebecca and Estlin, Tara and Fuchs, Thomas J. and Wagstaff, Kiri L.},
    year = {2012},
}
Download Endnote/RIS citation
TY - CONF
TI - Smart Cameras for Remote Science Survey
AU - Thompson, David R.
AU - Abbey, William
AU - Allwood, Abigail
AU - Bekker, Dmitriy
AU - Bornstein, Benjamin
AU - Cabrol, Nathalie A.
AU - Castano, Rebecca
AU - Estlin, Tara
AU - Fuchs, Thomas J.
AU - Wagstaff, Kiri L.
AB - Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels- distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.
C3 - Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS)
DA - 2012///
PY - 2012
UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.5807
ER -
Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels- distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.
Serum and Prostate Cancer Tissue Signatures of ERG Rearrangement Derived from Quantitative Analysis of the PTEN Conditional Knockout Mouse Proteome.
Niels J. Rupp, Igor Cima, Ralph Schiess, Peter J. Schüffler, Thomas J. Fuchs, Niklaus Frankhauser, Martin Kälin, Silke Gillessen, Ruedi Aebersold, Wilhelm Krek, Mark A. Rubin, Holger Moch and Peter J. Wild.
Symposium of the German Society for Pathology, 2012
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@inproceedings{rupp_serum_2012,
    title = {Serum and {Prostate} {Cancer} {Tissue} {Signatures} of {ERG} {Rearrangement} {Derived} from {Quantitative} {Analysis} of the {PTEN} {Conditional} {Knockout} {Mouse} {Proteome}},
    abstract = {Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers.
    Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement.
    Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41\% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort).
    Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.},
    booktitle = {Symposium of the {German} {Society} for {Pathology}},
    author = {Rupp, Niels J. and Cima, Igor and Schiess, Ralph and Sch\"uffler, Peter J. and Fuchs, Thomas J. and Frankhauser, Niklaus and K\"alin, Martin and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm and Rubin, Mark A. and Moch, Holger and Wild, Peter J.},
    year = {2012}
}
Download Endnote/RIS citation
TY - CONF
TI - Serum and Prostate Cancer Tissue Signatures of ERG Rearrangement Derived from Quantitative Analysis of the PTEN Conditional Knockout Mouse Proteome
AU - Rupp, Niels J.
AU - Cima, Igor
AU - Schiess, Ralph
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Frankhauser, Niklaus
AU - Kälin, Martin
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
AU - Rubin, Mark A.
AU - Moch, Holger
AU - Wild, Peter J.
AB - Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers.
Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement.
Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort).
Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.
C3 - Symposium of the German Society for Pathology
DA - 2012///
PY - 2012
ER -
Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers. Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement. Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort). Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.
A High-Throughput Metabolomics Method to Predict High Concentration Cytotoxicity of Drugs from Low Concentration Profiles.
Stephanie Heux, Thomas J. Fuchs, Joachim Buhmann, Nicola Zamboni and Uwe Sauer.
Metabolomics, vol. 8, 3, p. 433-443, 2012
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@article{heux_high-throughput_2012,
    title = {A {High}-{Throughput} {Metabolomics} {Method} to {Predict} {High} {Concentration} {Cytotoxicity} of {Drugs} from {Low} {Concentration} {Profiles}},
    volume = {8},
    issn = {1573-3882},
    url = {http://dx.doi.org/10.1007/s11306-011-0386-0},
    doi = {10.1007/s11306-011-0386-0},
    number = {3},
    journal = {Metabolomics},
    author = {Heux, Stephanie and Fuchs, Thomas J. and Buhmann, Joachim and Zamboni, Nicola and Sauer, Uwe},
    year = {2012},
    keywords = {Biomedical and Life Sciences},
    pages = {433--443}
}
Download Endnote/RIS citation
TY - JOUR
TI - A High-Throughput Metabolomics Method to Predict High Concentration Cytotoxicity of Drugs from Low Concentration Profiles
AU - Heux, Stephanie
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim
AU - Zamboni, Nicola
AU - Sauer, Uwe
T2 - Metabolomics
DA - 2012///
PY - 2012
DO - 10.1007/s11306-011-0386-0
VL - 8
IS - 3
SP - 433
EP - 443
SN - 1573-3882
UR - http://dx.doi.org/10.1007/s11306-011-0386-0
KW - Biomedical and Life Sciences
ER -
TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting.
Peter J. Schueffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI, 2012
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@inproceedings{schueffler_tmarker:_2012,
    title = {{TMARKER}: {A} {User}-{Friendly} {Open}-{Source} {Assistance} for {Tma} {Grading} and {Cell} {Counting}},
    booktitle = {Histopathology {Image} {Analysis} ({HIMA}) {Workshop} at the 15th {International} {Conference} on {Medical} {Image} {Computing} and {Computer} {Assisted} {Intervention} {MICCAI}},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2012}
}
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TY - CONF
TI - TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
C3 - Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI
DA - 2012///
PY - 2012
ER -
A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma: A Large-Scale Tissue-Microarray Study with Two Independent Patient Cohorts.
Stefanie Meyer, Thomas J. Fuchs, Anja K. Bosserhoff, Ferdinand Hofstädter, Armin Pauer, Volker Roth, Joachim M. Buhmann, Ingrid Moll, Nikos Anagnostou, Johanna M. Brandner, Kristian Ikenberg, Holger Moch, Michael Landthaler, Thomas Vogt and Peter J. Wild.
PLoS ONE, vol. 7, 6, p. e38222, 2012
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@article{meyer_seven-marker_2012,
    title = {A {Seven}-{Marker} {Signature} and {Clinical} {Outcome} in {Malignant} {Melanoma}: {A} {Large}-{Scale} {Tissue}-{Microarray} {Study} with {Two} {Independent} {Patient} {Cohorts}},
    volume = {7},
    shorttitle = {A {Seven}-{Marker} {Signature} and {Clinical} {Outcome} in {Malignant} {Melanoma}},
    url = {http://dx.doi.org/10.1371/journal.pone.0038222},
    doi = {10.1371/journal.pone.0038222},
    abstract = {BackgroundCurrent staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment.Methods and FindingsUsing tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation, cell adhesion, signaling and metabolism. A marker selection procedure based on univariate Cox regression and multiple testing correction was employed to correlate the IHC expression data with the clinical follow-up (overall and recurrence-free survival). The model was thoroughly evaluated with two different cross validation experiments, a permutation test and a multivariate Cox regression analysis. In addition, the predictive power of the identified marker signature was validated on a second independent external test cohort (n = 225). A signature of seven biomarkers (Bax, Bcl-X, PTEN, COX-2, loss of β-Catenin, loss of MTAP, and presence of CD20 positive B-lymphocytes) was found to be an independent negative predictor for overall and recurrence-free survival in patients with MM. The seven-marker signature could also predict a high risk of disease recurrence in patients with localized primary MM stage pT1-2 (tumor thickness ≤2.00 mm). In particular, three of these markers (MTAP, COX-2, Bcl-X) were shown to offer direct therapeutic implications.ConclusionsThe seven-marker signature might serve as a prognostic tool enabling physicians to selectively triage, at the time of diagnosis, the subset of high recurrence risk stage I–II patients for adjuvant therapy. Selective treatment of those patients that are more likely to develop distant metastatic disease could potentially lower the burden of untreatable metastatic melanoma and revolutionize the therapeutic management of MM.},
    number = {6},
    urldate = {2016-01-03TZ},
    journal = {PLoS ONE},
    author = {Meyer, Stefanie and Fuchs, Thomas J. and Bosserhoff, Anja K. and Hofst\"adter, Ferdinand and Pauer, Armin and Roth, Volker and Buhmann, Joachim M. and Moll, Ingrid and Anagnostou, Nikos and Brandner, Johanna M. and Ikenberg, Kristian and Moch, Holger and Landthaler, Michael and Vogt, Thomas and Wild, Peter J.},
    year = {2012},
    pages = {e38222}
}
Download Endnote/RIS citation
TY - JOUR
TI - A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma: A Large-Scale Tissue-Microarray Study with Two Independent Patient Cohorts
AU - Meyer, Stefanie
AU - Fuchs, Thomas J.
AU - Bosserhoff, Anja K.
AU - Hofstädter, Ferdinand
AU - Pauer, Armin
AU - Roth, Volker
AU - Buhmann, Joachim M.
AU - Moll, Ingrid
AU - Anagnostou, Nikos
AU - Brandner, Johanna M.
AU - Ikenberg, Kristian
AU - Moch, Holger
AU - Landthaler, Michael
AU - Vogt, Thomas
AU - Wild, Peter J.
T2 - PLoS ONE
AB - BackgroundCurrent staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment.Methods and FindingsUsing tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation, cell adhesion, signaling and metabolism. A marker selection procedure based on univariate Cox regression and multiple testing correction was employed to correlate the IHC expression data with the clinical follow-up (overall and recurrence-free survival). The model was thoroughly evaluated with two different cross validation experiments, a permutation test and a multivariate Cox regression analysis. In addition, the predictive power of the identified marker signature was validated on a second independent external test cohort (n = 225). A signature of seven biomarkers (Bax, Bcl-X, PTEN, COX-2, loss of β-Catenin, loss of MTAP, and presence of CD20 positive B-lymphocytes) was found to be an independent negative predictor for overall and recurrence-free survival in patients with MM. The seven-marker signature could also predict a high risk of disease recurrence in patients with localized primary MM stage pT1-2 (tumor thickness ≤2.00 mm). In particular, three of these markers (MTAP, COX-2, Bcl-X) were shown to offer direct therapeutic implications.ConclusionsThe seven-marker signature might serve as a prognostic tool enabling physicians to selectively triage, at the time of diagnosis, the subset of high recurrence risk stage I–II patients for adjuvant therapy. Selective treatment of those patients that are more likely to develop distant metastatic disease could potentially lower the burden of untreatable metastatic melanoma and revolutionize the therapeutic management of MM.
DA - 2012///
PY - 2012
DO - 10.1371/journal.pone.0038222
DP - PLoS Journals
VL - 7
IS - 6
SP - e38222
J2 - PLoS ONE
ST - A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma
UR - http://dx.doi.org/10.1371/journal.pone.0038222
Y2 - 2016/01/03/T15:45:02Z
ER -
BackgroundCurrent staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment.Methods and FindingsUsing tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation, cell adhesion, signaling and metabolism. A marker selection procedure based on univariate Cox regression and multiple testing correction was employed to correlate the IHC expression data with the clinical follow-up (overall and recurrence-free survival). The model was thoroughly evaluated with two different cross validation experiments, a permutation test and a multivariate Cox regression analysis. In addition, the predictive power of the identified marker signature was validated on a second independent external test cohort (n = 225). A signature of seven biomarkers (Bax, Bcl-X, PTEN, COX-2, loss of β-Catenin, loss of MTAP, and presence of CD20 positive B-lymphocytes) was found to be an independent negative predictor for overall and recurrence-free survival in patients with MM. The seven-marker signature could also predict a high risk of disease recurrence in patients with localized primary MM stage pT1-2 (tumor thickness ≤2.00 mm). In particular, three of these markers (MTAP, COX-2, Bcl-X) were shown to offer direct therapeutic implications.ConclusionsThe seven-marker signature might serve as a prognostic tool enabling physicians to selectively triage, at the time of diagnosis, the subset of high recurrence risk stage I–II patients for adjuvant therapy. Selective treatment of those patients that are more likely to develop distant metastatic disease could potentially lower the burden of untreatable metastatic melanoma and revolutionize the therapeutic management of MM.
Combined Shape, Appearance and Silhouette for Simultaneous Manipulator and Object Tracking.
Paul Hebert, Nicolas Hudson, Jeremy Ma, Thomas Howard, Thomas J. Fuchs, Max Bajracharya and Joel Burdick.
IEEE International Conference on Robotics and Automation (ICRA), p. 2405-2412, 2012
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@inproceedings{hebert_combined_2012,
    title = {Combined {Shape}, {Appearance} and {Silhouette} for {Simultaneous} {Manipulator} and {Object} {Tracking}},
    url = {http://robotics.caltech.edu/wiki/images/d/d3/HebertICRA12.pdf},
    doi = {10.1109/ICRA.2012.6225084},
    booktitle = {{IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
    author = {Hebert, Paul and Hudson, Nicolas and Ma, Jeremy and Howard, Thomas and Fuchs, Thomas J. and Bajracharya, Max and Burdick, Joel},
    year = {2012},
    pages = {2405--2412}
}
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TY - CONF
TI - Combined Shape, Appearance and Silhouette for Simultaneous Manipulator and Object Tracking
AU - Hebert, Paul
AU - Hudson, Nicolas
AU - Ma, Jeremy
AU - Howard, Thomas
AU - Fuchs, Thomas J.
AU - Bajracharya, Max
AU - Burdick, Joel
C3 - IEEE International Conference on Robotics and Automation (ICRA)
DA - 2012///
PY - 2012
DO - 10.1109/ICRA.2012.6225084
SP - 2405
EP - 2412
UR - http://robotics.caltech.edu/wiki/images/d/d3/HebertICRA12.pdf
ER -
TextureCam: Autonomous Image Analysis for Astrobiology Survey.
David R. Thompson, Abigail Allwood, Dmitriy Bekker, Nathalie A. Cabrol, Tara Estlin, Thomas J. Fuchs and Kiri L. Wagstaff.
43rd Lunar and Planetary Science Conference, vol. 43, p. 1659, 2012
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@inproceedings{thompson_texturecam:_2012,
    title = {{TextureCam}: {Autonomous} {Image} {Analysis} for {Astrobiology} {Survey}},
    volume = {43},
    url = {http://adsabs.harvard.edu/abs/2012LPI....43.1659T},
    booktitle = {43rd {Lunar} and {Planetary} {Science} {Conference}},
    author = {Thompson, David R. and Allwood, Abigail and Bekker, Dmitriy and Cabrol, Nathalie A. and Estlin, Tara and Fuchs, Thomas J. and Wagstaff, Kiri L.},
    year = {2012},
    pages = {1659}
}
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TY - CONF
TI - TextureCam: Autonomous Image Analysis for Astrobiology Survey
AU - Thompson, David R.
AU - Allwood, Abigail
AU - Bekker, Dmitriy
AU - Cabrol, Nathalie A.
AU - Estlin, Tara
AU - Fuchs, Thomas J.
AU - Wagstaff, Kiri L.
C3 - 43rd Lunar and Planetary Science Conference
DA - 2012///
PY - 2012
VL - 43
SP - 1659
UR - http://adsabs.harvard.edu/abs/2012LPI....43.1659T
ER -
Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer.
Igor Cima, Ralph Schiess, Peter Wild, Martin Kaelin, Peter Schueffler, Vinzenz Lange, Paola Picotti, Reto Ossola, Arnoud Templeton, Olga Schubert, Thomas J. Fuchs, Thomas Leippold, Stephen Wyler, Jens Zehetner, Wolfram Jochum, Joachim Buhmann, Thomas Cerny, Holger Moch, Silke Gillessen, Ruedi Aebersold and Wilhelm Krek.
PNAS: Proceedings of the National Academy of Sciences, vol. 108, 8, p. 3342-3347, 2011
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@article{cima_cancer_2011,
    title = {Cancer {Genetics}-{Guided} {Discovery} of {Serum} {Biomarker} {Signatures} for {Diagnosis} and {Prognosis} of {Prostate} {Cancer}},
    volume = {108},
    url = {http://www.pnas.org/content/108/8/3342.abstract},
    doi = {10.1073/pnas.1013699108},
    abstract = {A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.},
    number = {8},
    journal = {PNAS: Proceedings of the National Academy of Sciences},
    author = {Cima, Igor and Schiess, Ralph and Wild, Peter and Kaelin, Martin and Schueffler, Peter and Lange, Vinzenz and Picotti, Paola and Ossola, Reto and Templeton, Arnoud and Schubert, Olga and Fuchs, Thomas J. and Leippold, Thomas and Wyler, Stephen and Zehetner, Jens and Jochum, Wolfram and Buhmann, Joachim and Cerny, Thomas and Moch, Holger and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm},
    year = {2011},
    pages = {3342--3347}
}
Download Endnote/RIS citation
TY - JOUR
TI - Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer
AU - Cima, Igor
AU - Schiess, Ralph
AU - Wild, Peter
AU - Kaelin, Martin
AU - Schueffler, Peter
AU - Lange, Vinzenz
AU - Picotti, Paola
AU - Ossola, Reto
AU - Templeton, Arnoud
AU - Schubert, Olga
AU - Fuchs, Thomas J.
AU - Leippold, Thomas
AU - Wyler, Stephen
AU - Zehetner, Jens
AU - Jochum, Wolfram
AU - Buhmann, Joachim
AU - Cerny, Thomas
AU - Moch, Holger
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
T2 - PNAS: Proceedings of the National Academy of Sciences
AB - A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
DA - 2011///
PY - 2011
DO - 10.1073/pnas.1013699108
VL - 108
IS - 8
SP - 3342
EP - 3347
UR - http://www.pnas.org/content/108/8/3342.abstract
ER -
A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
Aerosols Transmit Prions to Immunocompetent and Immunodeficient Mice.
Johannes Haybaeck, Mathias Heikenwalder, Britta Klevenz, Petra Schwarz, Ilan Margalith, Claire Bridel, Kirsten Mertz, Elizabeta Zirdum, Benjamin Petsch, Thomas J. Fuchs, Lothar Stitz and Adriano Aguzzi.
PLoS Pathog, vol. 7, 1, p. e1001257, 2011
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@article{haybaeck_aerosols_2011,
    title = {Aerosols {Transmit} {Prions} to {Immunocompetent} and {Immunodeficient} {Mice}},
    volume = {7},
    url = {http://dx.doi.org/10.1371/journal.ppat.1001257},
    doi = {10.1371/journal.ppat.1001257},
    abstract = {Author Summary
    Prions, which are the cause of fatal neurodegenerative disorders termed transmissible spongiform encephalopathies (TSEs), can be experimentally or naturally transmitted via prion-contaminated food, blood, milk, saliva, feces and urine. Here we demonstrate that prions can be transmitted through aerosols in mice. This also occurs in the absence of immune cells as demonstrated by experiments with mice lacking B-, T-, follicular dendritic cells (FDCs), lymphotoxin signaling or with complement-deficient mice. Therefore, a functionally intact immune system is not strictly needed for aerogenic prion infection. These results suggest that current biosafety guidelines applied in diagnostic and scientific laboratories ought to include prion aerosols as a potential vector for prion infection.},
    number = {1},
    urldate = {2016-01-03TZ},
    journal = {PLoS Pathog},
    author = {Haybaeck, Johannes and Heikenwalder, Mathias and Klevenz, Britta and Schwarz, Petra and Margalith, Ilan and Bridel, Claire and Mertz, Kirsten and Zirdum, Elizabeta and Petsch, Benjamin and Fuchs, Thomas J. and Stitz, Lothar and Aguzzi, Adriano},
    year = {2011},
    pages = {e1001257}
}
Download Endnote/RIS citation
TY - JOUR
TI - Aerosols Transmit Prions to Immunocompetent and Immunodeficient Mice
AU - Haybaeck, Johannes
AU - Heikenwalder, Mathias
AU - Klevenz, Britta
AU - Schwarz, Petra
AU - Margalith, Ilan
AU - Bridel, Claire
AU - Mertz, Kirsten
AU - Zirdum, Elizabeta
AU - Petsch, Benjamin
AU - Fuchs, Thomas J.
AU - Stitz, Lothar
AU - Aguzzi, Adriano
T2 - PLoS Pathog
AB - Author Summary
Prions, which are the cause of fatal neurodegenerative disorders termed transmissible spongiform encephalopathies (TSEs), can be experimentally or naturally transmitted via prion-contaminated food, blood, milk, saliva, feces and urine. Here we demonstrate that prions can be transmitted through aerosols in mice. This also occurs in the absence of immune cells as demonstrated by experiments with mice lacking B-, T-, follicular dendritic cells (FDCs), lymphotoxin signaling or with complement-deficient mice. Therefore, a functionally intact immune system is not strictly needed for aerogenic prion infection. These results suggest that current biosafety guidelines applied in diagnostic and scientific laboratories ought to include prion aerosols as a potential vector for prion infection.
DA - 2011///
PY - 2011
DO - 10.1371/journal.ppat.1001257
DP - PLoS Journals
VL - 7
IS - 1
SP - e1001257
J2 - PLoS Pathog
UR - http://dx.doi.org/10.1371/journal.ppat.1001257
Y2 - 2016/01/03/T16:08:54Z
ER -
Author Summary Prions, which are the cause of fatal neurodegenerative disorders termed transmissible spongiform encephalopathies (TSEs), can be experimentally or naturally transmitted via prion-contaminated food, blood, milk, saliva, feces and urine. Here we demonstrate that prions can be transmitted through aerosols in mice. This also occurs in the absence of immune cells as demonstrated by experiments with mice lacking B-, T-, follicular dendritic cells (FDCs), lymphotoxin signaling or with complement-deficient mice. Therefore, a functionally intact immune system is not strictly needed for aerogenic prion infection. These results suggest that current biosafety guidelines applied in diagnostic and scientific laboratories ought to include prion aerosols as a potential vector for prion infection.
Computational Pathology: Challenges and Promises for Tissue Analysis.
Thomas J. Fuchs and Joachim M. Buhmann.
Computerized Medical Imaging and Graphics, vol. 35, 7–8, p. 515-530, 2011
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@article{fuchs_computational_2011,
    title = {Computational {Pathology}: {Challenges} and {Promises} for {Tissue} {Analysis}},
    volume = {35},
    issn = {0895-6111},
    shorttitle = {Computational pathology},
    url = {http://www.sciencedirect.com/science/article/pii/S0895611111000383},
    doi = {10.1016/j.compmedimag.2011.02.006},
    abstract = {The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.},
    number = {7–8},
    urldate = {2012-03-08TZ},
    journal = {Computerized Medical Imaging and Graphics},
    author = {Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2011},
    keywords = {Cancer research, Computational pathology, Medical imaging, Survival statistics, Whole, Whole slide imaging, imaging, machine learning, slide},
    pages = {515--530}
}
Download Endnote/RIS citation
TY - JOUR
TI - Computational Pathology: Challenges and Promises for Tissue Analysis
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
T2 - Computerized Medical Imaging and Graphics
AB - The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
DA - 2011///
PY - 2011
DO - 10.1016/j.compmedimag.2011.02.006
DP - ScienceDirect
VL - 35
IS - 7–8
SP - 515
EP - 530
SN - 0895-6111
ST - Computational pathology
UR - http://www.sciencedirect.com/science/article/pii/S0895611111000383
Y2 - 2012/03/08/T15:27:48Z
KW - Cancer research
KW - Computational pathology
KW - Medical imaging
KW - Survival statistics
KW - Whole
KW - Whole slide imaging
KW - imaging
KW - machine learning
KW - slide
ER -
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data.
Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs and Volker Roth.
Proceedings of the 27th International Conference on Machine Learning, p. 1111-1118, ICML'10, 2010
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@inproceedings{vogt_translation-invariant_2010,
    series = {{ICML}'10},
    title = {The {Translation}-invariant {Wishart}-{Dirichlet} {Process} for {Clustering} {Distance} {Data}},
    url = {http://www.icml2010.org/papers/248.pdf},
    booktitle = {Proceedings of the 27th {International} {Conference} on {Machine} {Learning}},
    author = {Vogt, Julia E. and Prabhakaran, Sandhya and Fuchs, Thomas J. and Roth, Volker},
    year = {2010},
    pages = {1111--1118}
}
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TY - CONF
TI - The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data
AU - Vogt, Julia E.
AU - Prabhakaran, Sandhya
AU - Fuchs, Thomas J.
AU - Roth, Volker
T3 - ICML'10
C3 - Proceedings of the 27th International Conference on Machine Learning
DA - 2010///
PY - 2010
SP - 1111
EP - 1118
UR - http://www.icml2010.org/papers/248.pdf
ER -
Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images.
Verena Kaynig, Thomas J. Fuchs and Joachim M. Buhmann.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 0, p. 2902-2909, IEEE Computer Society, ISBN 978-1-4244-6984-0, 2010
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@inproceedings{kaynig_neuron_2010,
    address = {Los Alamitos, CA, USA},
    title = {Neuron {Geometry} {Extraction} by {Perceptual} {Grouping} in {ssTEM} {Images}},
    volume = {0},
    isbn = {978-1-4244-6984-0},
    url = {http://www.computer.org/portal/web/csdl/doi/10.1109/CVPR.2010.5540029},
    doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5540029},
    booktitle = {{IEEE} {Computer} {Society} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
    publisher = {IEEE Computer Society},
    author = {Kaynig, Verena and Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2010},
    pages = {2902--2909}
}
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TY - CONF
TI - Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images
AU - Kaynig, Verena
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
C1 - Los Alamitos, CA, USA
C3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DA - 2010///
PY - 2010
DO - http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5540029
VL - 0
SP - 2902
EP - 2909
PB - IEEE Computer Society
SN - 978-1-4244-6984-0
UR - http://www.computer.org/portal/web/csdl/doi/10.1109/CVPR.2010.5540029
ER -
TAK1 Suppresses a NEMO-Dependent but NF-kappaB-Independent Pathway to Liver Cancer.
Kira Bettermann, Mihael Vucur, Johannes Haybaeck, Christiane Koppe, Joern Janssen, Felix Heymann, Achim Weber, Ralf Weiskirchen, Christian Liedtke, Nikolaus Gassler, Michael Mueller, Rita de Vos, Monika Julia Wolf, Yannick Boege, Gitta Maria Seleznik, Nicolas Zeller, Daniel Erny, Thomas J. Fuchs, Stefan Zoller, Stefano Cairo, Marie-Annick Buendia, Marco Prinz, Shizuo Akira, Frank Tacke, Mathias Heikenwaelder, Christian Trautwein and Tom Luedde.
Cancer Cell, vol. 17, 5, p. 481–496, 2010
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@article{bettermann_tak1_2010,
    title = {{TAK}1 {Suppresses} a {NEMO}-{Dependent} but {NF}-{kappaB}-{Independent} {Pathway} to {Liver} {Cancer}},
    volume = {17},
    url = {http://www.ncbi.nlm.nih.gov/pubmed/20478530},
    number = {5},
    journal = {Cancer Cell},
    author = {Bettermann, Kira and Vucur, Mihael and Haybaeck, Johannes and Koppe, Christiane and Janssen, Joern and Heymann, Felix and Weber, Achim and Weiskirchen, Ralf and Liedtke, Christian and Gassler, Nikolaus and Mueller, Michael and de Vos, Rita and Wolf, Monika Julia and Boege, Yannick and Seleznik, Gitta Maria and Zeller, Nicolas and Erny, Daniel and Fuchs, Thomas J. and Zoller, Stefan and Cairo, Stefano and Buendia, Marie-Annick and Prinz, Marco and Akira, Shizuo and Tacke, Frank and Heikenwaelder, Mathias and Trautwein, Christian and Luedde, Tom},
    year = {2010},
    pages = {481--496}
}
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TY - JOUR
TI - TAK1 Suppresses a NEMO-Dependent but NF-kappaB-Independent Pathway to Liver Cancer
AU - Bettermann, Kira
AU - Vucur, Mihael
AU - Haybaeck, Johannes
AU - Koppe, Christiane
AU - Janssen, Joern
AU - Heymann, Felix
AU - Weber, Achim
AU - Weiskirchen, Ralf
AU - Liedtke, Christian
AU - Gassler, Nikolaus
AU - Mueller, Michael
AU - de Vos, Rita
AU - Wolf, Monika Julia
AU - Boege, Yannick
AU - Seleznik, Gitta Maria
AU - Zeller, Nicolas
AU - Erny, Daniel
AU - Fuchs, Thomas J.
AU - Zoller, Stefan
AU - Cairo, Stefano
AU - Buendia, Marie-Annick
AU - Prinz, Marco
AU - Akira, Shizuo
AU - Tacke, Frank
AU - Heikenwaelder, Mathias
AU - Trautwein, Christian
AU - Luedde, Tom
T2 - Cancer Cell
DA - 2010///
PY - 2010
VL - 17
IS - 5
SP - 481
EP - 496
UR - http://www.ncbi.nlm.nih.gov/pubmed/20478530
ER -
Infinite Mixture-of-Experts Model for Sparse Survival Regression with Application to Breast Cancer.
Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Joachim M. Buhmann and Volker Roth.
BMC Bioinformatics, vol. 11, 8, p. S8, 2010
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@article{raman_infinite_2010,
    title = {Infinite {Mixture}-of-{Experts} {Model} for {Sparse} {Survival} {Regression} with {Application} to {Breast} {Cancer}},
    volume = {11},
    issn = {1471-2105},
    url = {http://dx.doi.org/10.1186/1471-2105-11-S8-S8},
    doi = {10.1186/1471-2105-11-S8-S8},
    abstract = {We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.},
    number = {8},
    urldate = {2016-01-03TZ},
    journal = {BMC Bioinformatics},
    author = {Raman, Sudhir and Fuchs, Thomas J. and Wild, Peter J. and Dahl, Edgar and Buhmann, Joachim M. and Roth, Volker},
    year = {2010},
    pages = {S8}
}
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TY - JOUR
TI - Infinite Mixture-of-Experts Model for Sparse Survival Regression with Application to Breast Cancer
AU - Raman, Sudhir
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AU - Dahl, Edgar
AU - Buhmann, Joachim M.
AU - Roth, Volker
T2 - BMC Bioinformatics
AB - We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.
DA - 2010///
PY - 2010
DO - 10.1186/1471-2105-11-S8-S8
DP - BioMed Central
VL - 11
IS - 8
SP - S8
J2 - BMC Bioinformatics
SN - 1471-2105
UR - http://dx.doi.org/10.1186/1471-2105-11-S8-S8
Y2 - 2016/01/03/T16:02:59Z
ER -
We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.
Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data.
Verena Kaynig, Thomas J. Fuchs and Joachim M. Buhmann.
Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II, p. 209–216, MICCAI'10, Springer-Verlag, Berlin, Heidelberg, ISBN 3-642-15744-0 978-3-642-15744-8, 2010
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@inproceedings{kaynig_geometrical_2010,
    address = {Beijing, China},
    series = {{MICCAI}'10},
    title = {Geometrical {Consistent} 3D {Tracing} of {Neuronal} {Processes} in {ssTEM} {Data}},
    isbn = {3-642-15744-0 978-3-642-15744-8},
    url = {http://portal.acm.org/citation.cfm?id=1928047.1928075},
    booktitle = {Proceedings of the 13th international conference on {Medical} image computing and computer-assisted intervention: {Part} {II}},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Kaynig, Verena and Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2010},
    pages = {209--216}
}
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TY - CONF
TI - Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data
AU - Kaynig, Verena
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
T3 - MICCAI'10
C1 - Beijing, China
C3 - Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
DA - 2010///
PY - 2010
SP - 209
EP - 216
PB - Springer-Verlag, Berlin, Heidelberg
SN - 3-642-15744-0 978-3-642-15744-8
UR - http://portal.acm.org/citation.cfm?id=1928047.1928075
ER -
Effect of Reader Experience on Variability, Evaluation Time and Accuracy of Coronary Plaque Detection with Computed Tomography Coronary Angiography.
Stefan Saur, Hatem Alkadhi, Paul Stolzmann, Stephan Baumueller, Sebastian Leschka, Hans Scheffel, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Philippe Cattin.
European Radiology, vol. 20, 7, p. 1599-1606, 2010
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@article{saur_effect_2010,
    title = {Effect of {Reader} {Experience} on {Variability}, {Evaluation} {Time} and {Accuracy} of {Coronary} {Plaque} {Detection} with {Computed} {Tomography} {Coronary} {Angiography}},
    volume = {20},
    issn = {0938-7994},
    url = {http://dx.doi.org/10.1007/s00330-009-1709-7},
    doi = {10.1007/s00330-009-1709-7},
    number = {7},
    journal = {European Radiology},
    author = {Saur, Stefan and Alkadhi, Hatem and Stolzmann, Paul and Baumueller, Stephan and Leschka, Sebastian and Scheffel, Hans and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Cattin, Philippe},
    year = {2010},
    keywords = {Medicine},
    pages = {1599--1606}
}
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TY - JOUR
TI - Effect of Reader Experience on Variability, Evaluation Time and Accuracy of Coronary Plaque Detection with Computed Tomography Coronary Angiography
AU - Saur, Stefan
AU - Alkadhi, Hatem
AU - Stolzmann, Paul
AU - Baumueller, Stephan
AU - Leschka, Sebastian
AU - Scheffel, Hans
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Cattin, Philippe
T2 - European Radiology
DA - 2010///
PY - 2010
DO - 10.1007/s00330-009-1709-7
VL - 20
IS - 7
SP - 1599
EP - 1606
SN - 0938-7994
UR - http://dx.doi.org/10.1007/s00330-009-1709-7
KW - Medicine
ER -
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma.
Peter J. Schueffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann.
Proceedings of the 32nd DAGM conference on Pattern recognition, p. 202–211, Springer-Verlag, Berlin, Heidelberg, ISBN 3-642-15985-0 978-3-642-15985-5, 2010
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@inproceedings{schueffler_computational_2010,
    address = {Darmstadt, Germany},
    title = {Computational {TMA} {Analysis} and {Cell} {Nucleus} {Classification} of {Renal} {Cell} {Carcinoma}},
    isbn = {3-642-15985-0 978-3-642-15985-5},
    url = {http://portal.acm.org/citation.cfm?id=1926258.1926281},
    abstract = {We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.},
    booktitle = {Proceedings of the 32nd {DAGM} conference on {Pattern} recognition},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2010},
    pages = {202--211}
}
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TY - CONF
TI - Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng Soon
AU - Roth, Volker
AU - Buhmann, Joachim M.
AB - We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
C1 - Darmstadt, Germany
C3 - Proceedings of the 32nd DAGM conference on Pattern recognition
DA - 2010///
PY - 2010
SP - 202
EP - 211
PB - Springer-Verlag, Berlin, Heidelberg
SN - 3-642-15985-0 978-3-642-15985-5
UR - http://portal.acm.org/citation.cfm?id=1926258.1926281
ER -
We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
Detection of Urothelial Bladder Cancer Cells in Voided Urine Can Be Improved by a Combination of Cytology and Standardized Microsatellite Analysis.
Peter J. Wild, Thomas J. Fuchs, Robert Stoehr, Dieter Zimmermann, Simona Frigerio, Barbara Padberg, Inbal Steiner, Ellen C. Zwarthoff, Maximilian Burger, Stefan Denzinger, Ferdinand Hofstaedter, Glen Kristiansen, Thomas Hermanns, Hans-Helge Seifert, Maurizio Provenzano, Tullio Sulser, Volker Roth, Joachim M. Buhmann, Holger Moch and Arndt Hartmann.
Cancer Epidemiol Biomarkers Prev, vol. 18, 6, p. 1798-1806, 2009
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@article{wild_detection_2009,
    title = {Detection of {Urothelial} {Bladder} {Cancer} {Cells} in {Voided} {Urine} {Can} {Be} {Improved} by a {Combination} of {Cytology} and {Standardized} {Microsatellite} {Analysis}},
    volume = {18},
    url = {http://cebp.aacrjournals.org/cgi/content/abstract/18/6/1798},
    doi = {10.1158/1055-9965.EPI-09-0099},
    abstract = {Purpose: To evaluate molecular and immunohistochemical markers to develop a molecular grading of urothelial bladder cancer and to test these markers in voided urine samples.
    Experimental Design: 255 consecutive biopsies from primary bladder cancer patients were evaluated on a tissue microarray. The clinical parameters gender, age, adjacent carcinoma in situ, and multifocality were collected. UroVysion fluorescence in situ hybridization (FISH) was done. Expression of cytokeratin 20, MIB1, and TP53 was analyzed by immunohistochemistry. Fibroblast growth factor receptor 3 (FGFR3) status was studied by SNaPshot mutation detection. Results were correlated with clinical outcome by Cox regression analysis. To assess the predictive power of different predictor subsets to detect high grade and tumor invasion, logistic regression models were learned. Additionally, voided urine samples of 119 patients were investigated. After cytologic examination, urine samples were matched with their biopsies and analyzed for loss of heterozygosity (LOH), FGFR3 mutation, polysomy, and p16 deletion using UroVysion FISH. Receiver operator characteristic curves for various predictor subsets were plotted.
    Results: In biopsies, high grade and solid growth pattern were independent prognostic factors for overall survival. A model consisting of UroVysion FISH and FGFR3 status (FISH + FGFR3) predicted high grade significantly better compared with a recently proposed molecular grade (MIB1 + FGFR3). In voided urine, the combination of cytology with LOH analysis (CYTO + LOH) reached the highest diagnostic accuracy for the detection of bladder cancer cells and performed better than cytology alone (sensitivity of 88.2\% and specificity of 97.1\%).
    Conclusions: The combination of cytology with LOH analysis could reduce unpleasant cystoscopies for bladder cancer patients.},
    number = {6},
    journal = {Cancer Epidemiol Biomarkers Prev},
    author = {Wild, Peter J. and Fuchs, Thomas J. and Stoehr, Robert and Zimmermann, Dieter and Frigerio, Simona and Padberg, Barbara and Steiner, Inbal and Zwarthoff, Ellen C. and Burger, Maximilian and Denzinger, Stefan and Hofstaedter, Ferdinand and Kristiansen, Glen and Hermanns, Thomas and Seifert, Hans-Helge and Provenzano, Maurizio and Sulser, Tullio and Roth, Volker and Buhmann, Joachim M. and Moch, Holger and Hartmann, Arndt},
    year = {2009},
    pages = {1798--1806}
}
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TY - JOUR
TI - Detection of Urothelial Bladder Cancer Cells in Voided Urine Can Be Improved by a Combination of Cytology and Standardized Microsatellite Analysis
AU - Wild, Peter J.
AU - Fuchs, Thomas J.
AU - Stoehr, Robert
AU - Zimmermann, Dieter
AU - Frigerio, Simona
AU - Padberg, Barbara
AU - Steiner, Inbal
AU - Zwarthoff, Ellen C.
AU - Burger, Maximilian
AU - Denzinger, Stefan
AU - Hofstaedter, Ferdinand
AU - Kristiansen, Glen
AU - Hermanns, Thomas
AU - Seifert, Hans-Helge
AU - Provenzano, Maurizio
AU - Sulser, Tullio
AU - Roth, Volker
AU - Buhmann, Joachim M.
AU - Moch, Holger
AU - Hartmann, Arndt
T2 - Cancer Epidemiol Biomarkers Prev
AB - Purpose: To evaluate molecular and immunohistochemical markers to develop a molecular grading of urothelial bladder cancer and to test these markers in voided urine samples.
Experimental Design: 255 consecutive biopsies from primary bladder cancer patients were evaluated on a tissue microarray. The clinical parameters gender, age, adjacent carcinoma in situ, and multifocality were collected. UroVysion fluorescence in situ hybridization (FISH) was done. Expression of cytokeratin 20, MIB1, and TP53 was analyzed by immunohistochemistry. Fibroblast growth factor receptor 3 (FGFR3) status was studied by SNaPshot mutation detection. Results were correlated with clinical outcome by Cox regression analysis. To assess the predictive power of different predictor subsets to detect high grade and tumor invasion, logistic regression models were learned. Additionally, voided urine samples of 119 patients were investigated. After cytologic examination, urine samples were matched with their biopsies and analyzed for loss of heterozygosity (LOH), FGFR3 mutation, polysomy, and p16 deletion using UroVysion FISH. Receiver operator characteristic curves for various predictor subsets were plotted.
Results: In biopsies, high grade and solid growth pattern were independent prognostic factors for overall survival. A model consisting of UroVysion FISH and FGFR3 status (FISH + FGFR3) predicted high grade significantly better compared with a recently proposed molecular grade (MIB1 + FGFR3). In voided urine, the combination of cytology with LOH analysis (CYTO + LOH) reached the highest diagnostic accuracy for the detection of bladder cancer cells and performed better than cytology alone (sensitivity of 88.2% and specificity of 97.1%).
Conclusions: The combination of cytology with LOH analysis could reduce unpleasant cystoscopies for bladder cancer patients.
DA - 2009///
PY - 2009
DO - 10.1158/1055-9965.EPI-09-0099
VL - 18
IS - 6
SP - 1798
EP - 1806
UR - http://cebp.aacrjournals.org/cgi/content/abstract/18/6/1798
ER -
Purpose: To evaluate molecular and immunohistochemical markers to develop a molecular grading of urothelial bladder cancer and to test these markers in voided urine samples. Experimental Design: 255 consecutive biopsies from primary bladder cancer patients were evaluated on a tissue microarray. The clinical parameters gender, age, adjacent carcinoma in situ, and multifocality were collected. UroVysion fluorescence in situ hybridization (FISH) was done. Expression of cytokeratin 20, MIB1, and TP53 was analyzed by immunohistochemistry. Fibroblast growth factor receptor 3 (FGFR3) status was studied by SNaPshot mutation detection. Results were correlated with clinical outcome by Cox regression analysis. To assess the predictive power of different predictor subsets to detect high grade and tumor invasion, logistic regression models were learned. Additionally, voided urine samples of 119 patients were investigated. After cytologic examination, urine samples were matched with their biopsies and analyzed for loss of heterozygosity (LOH), FGFR3 mutation, polysomy, and p16 deletion using UroVysion FISH. Receiver operator characteristic curves for various predictor subsets were plotted. Results: In biopsies, high grade and solid growth pattern were independent prognostic factors for overall survival. A model consisting of UroVysion FISH and FGFR3 status (FISH + FGFR3) predicted high grade significantly better compared with a recently proposed molecular grade (MIB1 + FGFR3). In voided urine, the combination of cytology with LOH analysis (CYTO + LOH) reached the highest diagnostic accuracy for the detection of bladder cancer cells and performed better than cytology alone (sensitivity of 88.2% and specificity of 97.1%). Conclusions: The combination of cytology with LOH analysis could reduce unpleasant cystoscopies for bladder cancer patients.
Nuclear Detection of Y-Box Protein-1 (YB-1) Closely Associates with Progesterone Receptor Negativity and Is a Strong Adverse Survival Factor in Human Breast Cancer.
Edgar Dahl, Abdelaziz En-Nia, Frank Wiesmann, Renate Krings, Sonja Djudjaj, Elisabeth Breuer, Thomas J. Fuchs, Peter Wild, Arndt Hartmann, Sandra Dunn and Peter Mertens.
BMC Cancer, vol. 9, 1, p. 410, 2009
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@article{dahl_nuclear_2009,
    title = {Nuclear {Detection} of {Y}-{Box} {Protein}-1 ({YB}-1) {Closely} {Associates} with {Progesterone} {Receptor} {Negativity} and {Is} a {Strong} {Adverse} {Survival} {Factor} in {Human} {Breast} {Cancer}},
    volume = {9},
    issn = {1471-2407},
    url = {http://www.biomedcentral.com/1471-2407/9/410},
    doi = {10.1186/1471-2407-9-410},
    number = {1},
    journal = {BMC Cancer},
    author = {Dahl, Edgar and En-Nia, Abdelaziz and Wiesmann, Frank and Krings, Renate and Djudjaj, Sonja and Breuer, Elisabeth and Fuchs, Thomas J. and Wild, Peter and Hartmann, Arndt and Dunn, Sandra and Mertens, Peter},
    year = {2009},
    pages = {410}
}
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TY - JOUR
TI - Nuclear Detection of Y-Box Protein-1 (YB-1) Closely Associates with Progesterone Receptor Negativity and Is a Strong Adverse Survival Factor in Human Breast Cancer
AU - Dahl, Edgar
AU - En-Nia, Abdelaziz
AU - Wiesmann, Frank
AU - Krings, Renate
AU - Djudjaj, Sonja
AU - Breuer, Elisabeth
AU - Fuchs, Thomas J.
AU - Wild, Peter
AU - Hartmann, Arndt
AU - Dunn, Sandra
AU - Mertens, Peter
T2 - BMC Cancer
DA - 2009///
PY - 2009
DO - 10.1186/1471-2407-9-410
VL - 9
IS - 1
SP - 410
SN - 1471-2407
UR - http://www.biomedcentral.com/1471-2407/9/410
ER -
Prediction Rules for the Detection of Coronary Artery Plaques: Evidence from Cardiac CT.
Stefan C. Saur, Philippe C. Cattin, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Hatem Alkadhi.
Investigative Radiology, vol. 44, 8, p. 483-490, 2009
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@article{saur_prediction_2009,
    title = {Prediction {Rules} for the {Detection} of {Coronary} {Artery} {Plaques}: {Evidence} from {Cardiac} {CT}},
    volume = {44},
    url = {http://journals.lww.com/investigativeradiology/Abstract/2009/08000/Prediction_Rules_for_the_Detection_of_Coronary.8.aspx},
    doi = {http://dx.doi.org/10.1097/RLI.0b013e3181a8afc4},
    abstract = {Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques.
    Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining.
    Results: In 200 of 252 (79.4\%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6\%; 227 mixed, 18.5\%; 12 noncalcified, 1\%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6\% of the patients had no plaques at all; 31.7\% had plaques in the left coronary artery tree; 46.4\% had plaques both in left and right coronary arteries, whereas 1.2\% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P {\textless} 0.001) than the weighted random approach by revealing 48 initially missed plaques.
    Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.},
    number = {8},
    journal = {Investigative Radiology},
    author = {Saur, Stefan C. and Cattin, Philippe C. and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Alkadhi, Hatem},
    year = {2009},
    pages = {483--490}
}
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TY - JOUR
TI - Prediction Rules for the Detection of Coronary Artery Plaques: Evidence from Cardiac CT
AU - Saur, Stefan C.
AU - Cattin, Philippe C.
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Alkadhi, Hatem
T2 - Investigative Radiology
AB - Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques.
Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining.
Results: In 200 of 252 (79.4%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6%; 227 mixed, 18.5%; 12 noncalcified, 1%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6% of the patients had no plaques at all; 31.7% had plaques in the left coronary artery tree; 46.4% had plaques both in left and right coronary arteries, whereas 1.2% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P < 0.001) than the weighted random approach by revealing 48 initially missed plaques.
Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.
DA - 2009///
PY - 2009
DO - http://dx.doi.org/10.1097/RLI.0b013e3181a8afc4
VL - 44
IS - 8
SP - 483
EP - 490
UR - http://journals.lww.com/investigativeradiology/Abstract/2009/08000/Prediction_Rules_for_the_Detection_of_Coronary.8.aspx
ER -
Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques. Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining. Results: In 200 of 252 (79.4%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6%; 227 mixed, 18.5%; 12 noncalcified, 1%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6% of the patients had no plaques at all; 31.7% had plaques in the left coronary artery tree; 46.4% had plaques both in left and right coronary arteries, whereas 1.2% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P < 0.001) than the weighted random approach by revealing 48 initially missed plaques. Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.
The Bayesian Group-Lasso for Analyzing Contingency Tables.
Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl and Volker Roth.
Proceedings of the 26th Annual International Conference on Machine Learning, p. 881–888, ICML '09, ACM, New York, NY, USA, ISBN 978-1-60558-516-1, 2009
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@inproceedings{raman_bayesian_2009,
    address = {Montreal, Quebec, Canada},
    series = {{ICML} '09},
    title = {The {Bayesian} {Group}-{Lasso} for {Analyzing} {Contingency} {Tables}},
    isbn = {978-1-60558-516-1},
    url = {http://doi.acm.org/10.1145/1553374.1553487},
    doi = {10.1145/1553374.1553487},
    booktitle = {Proceedings of the 26th {Annual} {International} {Conference} on {Machine} {Learning}},
    publisher = {ACM, New York, NY, USA},
    author = {Raman, Sudhir and Fuchs, Thomas J. and Wild, Peter J. and Dahl, Edgar and Roth, Volker},
    year = {2009},
    pages = {881--888}
}
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TY - CONF
TI - The Bayesian Group-Lasso for Analyzing Contingency Tables
AU - Raman, Sudhir
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AU - Dahl, Edgar
AU - Roth, Volker
T3 - ICML '09
C1 - Montreal, Quebec, Canada
C3 - Proceedings of the 26th Annual International Conference on Machine Learning
DA - 2009///
PY - 2009
DO - 10.1145/1553374.1553487
SP - 881
EP - 888
PB - ACM, New York, NY, USA
SN - 978-1-60558-516-1
UR - http://doi.acm.org/10.1145/1553374.1553487
ER -
Prognostic Relevance of Wnt-Inhibitory Factor-1 (WIF1) and Dickkopf-3 (DKK3) Promoter Methylation in Human Breast Cancer.
Jurgen Veeck, Peter Wild, Thomas J. Fuchs, Peter Schueffler, Arndt Hartmann, Ruth Knuchel and Edgar Dahl.
BMC Cancer, vol. 9, 1, p. 217, 2009
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@article{veeck_prognostic_2009,
    title = {Prognostic {Relevance} of {Wnt}-{Inhibitory} {Factor}-1 ({WIF}1) and {Dickkopf}-3 ({DKK}3) {Promoter} {Methylation} in {Human} {Breast} {Cancer}},
    volume = {9},
    issn = {1471-2407},
    url = {http://www.biomedcentral.com/1471-2407/9/217},
    doi = {10.1186/1471-2407-9-217},
    abstract = {Background
    Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.
    Methods
    WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.
    Results
    WIF1 and DKK3 promoter methylation were detected in 63.3\% (95/150) and 61.3\% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0\% (0/19) and DKK3 methylation in 5.3\% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54\% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97\% OS after 10 years (p {\textless} 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58\%, compared with 78\% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95\% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95\% CI: 1.0–6.0; p = 0.047) in breast cancer.
    Conclusion
    Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.},
    number = {1},
    journal = {BMC Cancer},
    author = {Veeck, Jurgen and Wild, Peter and Fuchs, Thomas J. and Schueffler, Peter and Hartmann, Arndt and Knuchel, Ruth and Dahl, Edgar},
    year = {2009},
    pages = {217}
}
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TY - JOUR
TI - Prognostic Relevance of Wnt-Inhibitory Factor-1 (WIF1) and Dickkopf-3 (DKK3) Promoter Methylation in Human Breast Cancer
AU - Veeck, Jurgen
AU - Wild, Peter
AU - Fuchs, Thomas J.
AU - Schueffler, Peter
AU - Hartmann, Arndt
AU - Knuchel, Ruth
AU - Dahl, Edgar
T2 - BMC Cancer
AB - Background
Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.
Methods
WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.
Results
WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer.
Conclusion
Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.
DA - 2009///
PY - 2009
DO - 10.1186/1471-2407-9-217
VL - 9
IS - 1
SP - 217
SN - 1471-2407
UR - http://www.biomedcentral.com/1471-2407/9/217
ER -
Background Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease. Methods WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses. Results WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer. Conclusion Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.
Inter-Active Learning of Randomized Tree Ensembles for Object Detection.
Thomas J. Fuchs and Joachim M. Buhmann.
Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on Computer Vision, p. 1370–1377, ISBN 978-1-4244-4442-7, 2009
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@inproceedings{fuchs_inter-active_2009,
    title = {Inter-{Active} {Learning} of {Randomized} {Tree} {Ensembles} for {Object} {Detection}},
    isbn = {978-1-4244-4442-7},
    url = {http://dx.doi.org/10.1109/ICCVW.2009.5457452},
    doi = {http://dx.doi.org/10.1109/ICCVW.2009.5457452},
    booktitle = {Computer {Vision} {Workshops} ({ICCV} {Workshops}), 2009 {IEEE} 12th {International} {Conference} on {Computer} {Vision}},
    author = {Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2009},
    pages = {1370--1377}
}
Download Endnote/RIS citation
TY - CONF
TI - Inter-Active Learning of Randomized Tree Ensembles for Object Detection
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
C3 - Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on Computer Vision
DA - 2009///
PY - 2009
DO - http://dx.doi.org/10.1109/ICCVW.2009.5457452
SP - 1370
EP - 1377
SN - 978-1-4244-4442-7
UR - http://dx.doi.org/10.1109/ICCVW.2009.5457452
ER -
Guided Review by Frequent Itemset Mining: Additional Evidence for Plaque Detection.
Stefan C. Saur, Hatem Alkadhi, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Philippe C. Cattin.
International Journal of Computer Assisted Radiology and Surgery, vol. 4, 3, p. 263–271, 2009
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@article{saur_guided_2009,
    title = {Guided {Review} by {Frequent} {Itemset} {Mining}: {Additional} {Evidence} for {Plaque} {Detection}},
    volume = {4},
    url = {http://dx.doi.org/10.1007/s11548-009-0290-5},
    number = {3},
    journal = {International Journal of Computer Assisted Radiology and Surgery},
    author = {Saur, Stefan C. and Alkadhi, Hatem and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Cattin, Philippe C.},
    year = {2009},
    pages = {263--271}
}
Download Endnote/RIS citation
TY - JOUR
TI - Guided Review by Frequent Itemset Mining: Additional Evidence for Plaque Detection
AU - Saur, Stefan C.
AU - Alkadhi, Hatem
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Cattin, Philippe C.
T2 - International Journal of Computer Assisted Radiology and Surgery
DA - 2009///
PY - 2009
VL - 4
IS - 3
SP - 263
EP - 271
UR - http://dx.doi.org/10.1007/s11548-009-0290-5
ER -
Randomized Tree Ensembles for Object Detection in Computational Pathology.
Thomas J. Fuchs, Johannes Haybaeck, Peter J. Wild, Mathias Heikenwalder, Holger Moch, Adriano Aguzzi and Joachim M. Buhmann.
Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I, p. 367–378, ISVC '09, Springer-Verlag, Berlin, Heidelberg, ISBN 978-3-642-10330-8, 2009
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@inproceedings{fuchs_randomized_2009,
    address = {Las Vegas, Nevada},
    series = {{ISVC} '09},
    title = {Randomized {Tree} {Ensembles} for {Object} {Detection} in {Computational} {Pathology}},
    isbn = {978-3-642-10330-8},
    url = {http://dx.doi.org/10.1007/978-3-642-10331-5_35},
    doi = {http://dx.doi.org/10.1007/978-3-642-10331-5_35},
    booktitle = {Proceedings of the 5th {International} {Symposium} on {Advances} in {Visual} {Computing}: {Part} {I}},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Fuchs, Thomas J. and Haybaeck, Johannes and Wild, Peter J. and Heikenwalder, Mathias and Moch, Holger and Aguzzi, Adriano and Buhmann, Joachim M.},
    year = {2009},
    pages = {367--378}
}
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TY - CONF
TI - Randomized Tree Ensembles for Object Detection in Computational Pathology
AU - Fuchs, Thomas J.
AU - Haybaeck, Johannes
AU - Wild, Peter J.
AU - Heikenwalder, Mathias
AU - Moch, Holger
AU - Aguzzi, Adriano
AU - Buhmann, Joachim M.
T3 - ISVC '09
C1 - Las Vegas, Nevada
C3 - Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
DA - 2009///
PY - 2009
DO - http://dx.doi.org/10.1007/978-3-642-10331-5_35
SP - 367
EP - 378
PB - Springer-Verlag, Berlin, Heidelberg
SN - 978-3-642-10330-8
UR - http://dx.doi.org/10.1007/978-3-642-10331-5_35
ER -
Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue.
Xenofon E. Floros, Thomas J. Fuchs, Markus P. Rechsteiner, Giatgen Spinas, Holger Moch and Joachim M. Buhmann.
Medical Image Computing and Computer-Assisted Intervention MICCAI, p. 633-640, 2009
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@inproceedings{floros_graph-based_2009,
    title = {Graph-{Based} {Pancreatic} {Islet} {Segmentation} for {Early} {Type} 2 {Diabetes} {Mellitus} on {Histopathological} {Tissue}},
    url = {http://www.springerlink.com/index/2PG4628N77772L77.pdf},
    booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} {MICCAI}},
    author = {Floros, Xenofon E. and Fuchs, Thomas J. and Rechsteiner, Markus P. and Spinas, Giatgen and Moch, Holger and Buhmann, Joachim M.},
    year = {2009},
    pages = {633--640}
}
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TY - CONF
TI - Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue
AU - Floros, Xenofon E.
AU - Fuchs, Thomas J.
AU - Rechsteiner, Markus P.
AU - Spinas, Giatgen
AU - Moch, Holger
AU - Buhmann, Joachim M.
C3 - Medical Image Computing and Computer-Assisted Intervention MICCAI
DA - 2009///
PY - 2009
SP - 633
EP - 640
UR - http://www.springerlink.com/index/2PG4628N77772L77.pdf
ER -
Automatic and Robust Forearm Segmentation Using Graph Cuts.
Philipp Fuernstahl, Thomas J. Fuchs, Andreas Schweizer, Ladislav Nagy, Gabor Szekely and Matthias Harders.
5th IEEE International Symposium on Biomedical Imaging. ISBI 2008, p. 77–80, IEEE, ISBN 978-1-4244-2002-5, 2008
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@inproceedings{fuernstahl_automatic_2008,
    address = {Paris, France},
    title = {Automatic and {Robust} {Forearm} {Segmentation} {Using} {Graph} {Cuts}},
    isbn = {978-1-4244-2002-5},
    url = {http://dx.doi.org/10.1109/ISBI.2008.4540936},
    doi = {10.1109/ISBI.2008.4540936},
    abstract = {The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.},
    booktitle = {5th {IEEE} {International} {Symposium} on {Biomedical} {Imaging}. {ISBI} 2008},
    publisher = {IEEE},
    author = {Fuernstahl, Philipp and Fuchs, Thomas J. and Schweizer, Andreas and Nagy, Ladislav and Szekely, Gabor and Harders, Matthias},
    year = {2008},
    pages = {77--80}
}
Download Endnote/RIS citation
TY - CONF
TI - Automatic and Robust Forearm Segmentation Using Graph Cuts
AU - Fuernstahl, Philipp
AU - Fuchs, Thomas J.
AU - Schweizer, Andreas
AU - Nagy, Ladislav
AU - Szekely, Gabor
AU - Harders, Matthias
AB - The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.
C1 - Paris, France
C3 - 5th IEEE International Symposium on Biomedical Imaging. ISBI 2008
DA - 2008///
PY - 2008
DO - 10.1109/ISBI.2008.4540936
SP - 77
EP - 80
PB - IEEE
SN - 978-1-4244-2002-5
UR - http://dx.doi.org/10.1109/ISBI.2008.4540936
ER -
The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.
Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Cell Carcinoma.
Thomas J. Fuchs, Tilman Lange, Peter J. Wild, Holger Moch and Joachim M. Buhmann.
Pattern Recognition. DAGM 2008, vol. 5096, p. 173–182, Lecture Notes in Computer Science, Springer-Verlag, ISBN 978-3-540-69320-8, 2008
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@inproceedings{fuchs_weakly_2008,
    address = {Berlin, Heidelberg},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Weakly {Supervised} {Cell} {Nuclei} {Detection} and {Segmentation} on {Tissue} {Microarrays} of {Renal} {Cell} {Carcinoma}},
    volume = {5096},
    isbn = {978-3-540-69320-8},
    url = {http://www.springerlink.com/index/L380G56T1V230715.pdf},
    doi = {10.1007/978-3-540-69321-},
    booktitle = {Pattern {Recognition}. {DAGM} 2008},
    publisher = {Springer-Verlag},
    author = {Fuchs, Thomas J. and Lange, Tilman and Wild, Peter J. and Moch, Holger and Buhmann, Joachim M.},
    year = {2008},
    pages = {173--182}
}
Download Endnote/RIS citation
TY - CONF
TI - Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Cell Carcinoma
AU - Fuchs, Thomas J.
AU - Lange, Tilman
AU - Wild, Peter J.
AU - Moch, Holger
AU - Buhmann, Joachim M.
T3 - Lecture Notes in Computer Science
C1 - Berlin, Heidelberg
C3 - Pattern Recognition. DAGM 2008
DA - 2008///
PY - 2008
DO - 10.1007/978-3-540-69321-
VL - 5096
SP - 173
EP - 182
PB - Springer-Verlag
SN - 978-3-540-69320-8
UR - http://www.springerlink.com/index/L380G56T1V230715.pdf
ER -
Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients.
Thomas J. Fuchs, Peter J. Wild, Holger Moch and Joachim M. Buhmann.
Proceedings of the international conference on Medical Image Computing and Computer-Assisted Intervention MICCAI, vol. 5242, p. 1-8, Lecture Notes in Computer Science, Springer-Verlag, ISBN 978-3-540-85989-5, 2008
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@inproceedings{fuchs_computational_2008,
    address = {Berlin, Heidelberg},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Computational {Pathology} {Analysis} of {Tissue} {Microarrays} {Predicts} {Survival} of {Renal} {Clear} {Cell} {Carcinoma} {Patients}},
    volume = {5242},
    isbn = {978-3-540-85989-5},
    url = {http://dx.doi.org/10.1007/978-3-540-85990-1_1},
    doi = {10.1007/978-3-540-85990-1_1},
    booktitle = {Proceedings of the international conference on {Medical} {Image} {Computing} and {Computer}-{Assisted} {Intervention} {MICCAI}},
    publisher = {Springer-Verlag},
    author = {Fuchs, Thomas J. and Wild, Peter J. and Moch, Holger and Buhmann, Joachim M.},
    year = {2008},
    keywords = {Computer Science},
    pages = {1--8}
}
Download Endnote/RIS citation
TY - CONF
TI - Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AU - Moch, Holger
AU - Buhmann, Joachim M.
T3 - Lecture Notes in Computer Science
C1 - Berlin, Heidelberg
C3 - Proceedings of the international conference on Medical Image Computing and Computer-Assisted Intervention MICCAI
DA - 2008///
PY - 2008
DO - 10.1007/978-3-540-85990-1_1
VL - 5242
SP - 1
EP - 8
PB - Springer-Verlag
SN - 978-3-540-85989-5
UR - http://dx.doi.org/10.1007/978-3-540-85990-1_1
KW - Computer Science
ER -
Nuclear Karyopherin Alpha2 Expression Predicts Poor Survival in Patients with Advanced Breast Cancer Irrespective of Treatment Intensity.
Oleg Gluz, Peter Wild, Robert Meiler, Raihana Diallo-Danebrock, Evelyn Ting, Svjetlana Mohrmann, Gerhart Schuett, Edgar Dahl, Thomas J. Fuchs, Alexander Herr, Andreas Gaumann, Markus Frick, Christopher Poremba, Ulrike Anneliese Nitz and Arndt Hartmann.
International Journal of Cancer, vol. 123/6, IF 4,693, p. 1433–1438, 2008
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@article{gluz_nuclear_2008,
    title = {Nuclear {Karyopherin} {Alpha}2 {Expression} {Predicts} {Poor} {Survival} in {Patients} with {Advanced} {Breast} {Cancer} {Irrespective} of {Treatment} {Intensity}},
    volume = {123/6},
    issn = {1097-0215},
    url = {http://onlinelibrary.wiley.com/doi/10.1002/ijc.23628/full},
    doi = {10.1002/ijc.23628},
    number = {IF 4,693},
    journal = {International Journal of Cancer},
    author = {Gluz, Oleg and Wild, Peter and Meiler, Robert and Diallo-Danebrock, Raihana and Ting, Evelyn and Mohrmann, Svjetlana and Schuett, Gerhart and Dahl, Edgar and Fuchs, Thomas J. and Herr, Alexander and Gaumann, Andreas and Frick, Markus and Poremba, Christopher and Nitz, Ulrike Anneliese and Hartmann, Arndt},
    year = {2008},
    pages = {1433--1438}
}
Download Endnote/RIS citation
TY - JOUR
TI - Nuclear Karyopherin Alpha2 Expression Predicts Poor Survival in Patients with Advanced Breast Cancer Irrespective of Treatment Intensity
AU - Gluz, Oleg
AU - Wild, Peter
AU - Meiler, Robert
AU - Diallo-Danebrock, Raihana
AU - Ting, Evelyn
AU - Mohrmann, Svjetlana
AU - Schuett, Gerhart
AU - Dahl, Edgar
AU - Fuchs, Thomas J.
AU - Herr, Alexander
AU - Gaumann, Andreas
AU - Frick, Markus
AU - Poremba, Christopher
AU - Nitz, Ulrike Anneliese
AU - Hartmann, Arndt
T2 - International Journal of Cancer
DA - 2008///
PY - 2008
DO - 10.1002/ijc.23628
VL - 123/6
IS - IF 4,693
SP - 1433
EP - 1438
SN - 1097-0215
UR - http://onlinelibrary.wiley.com/doi/10.1002/ijc.23628/full
ER -
The Acute Coronary Syndrome – Pre-Hospital Diagnostic Quality.
Geza Gemes, Thomas J. Fuchs, Gernot Wildner, Freyja-Maria Smolle-Juettner, Josef Smolle, Kurt Stoschitzky and Gerhard Prause.
Resuscitation, vol. 66, 3, p. 323 - 330, 2005
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@article{gemes_acute_2005,
    title = {The {Acute} {Coronary} {Syndrome} – {Pre}-{Hospital} {Diagnostic} {Quality}},
    volume = {66},
    issn = {0300-9572},
    url = {http://www.sciencedirect.com/science/article/B6T19-4GV9846-3/2/bcd3755266ba68b84536b39803426401},
    doi = {DOI: 10.1016/j.resuscitation.2005.04.006},
    abstract = {Background and objective: In the Austrian emergency medical service (EMS), emergency medical technician-staffed and physician-staffed vehicles are in operation. Patients with suspected acute coronary syndromes (ACS) are treated in the pre-hospital phase and transported to the hospital by an emergency physician (EP). This study evaluates the diagnostic performance of EPs in ACS and the impact of this emergency system on the outcome of ACS in an urban area.
    Design: Retrospective case control study.
    Methods: All protocol sheets from the emergency physicians were searched for the diagnosis of ACS. The database of the emergency department (ED) was searched for patients with ACS as an admission diagnosis or ACS as discharge diagnosis. For patients admitted to an intensive care unit (ICU), the medical history from the ICU was reviewed. According to the diagnosis and the aggressiveness of therapy, patients were divided in five categories of severity at each stage of care (pre-hospital category, ED category, ICU category).
    Results: A total of 3585 patients was analysed. Only 17.8\% of the patients with ACS as the admission diagnosis and 20.3\% of the patients with ACS as the discharge diagnosis were transported by an EP. 46.8\% of the ACS diagnosis by EPs were confirmed in hospital. Patients transported by EPs showed a higher all-cause mortality in hospital (1.6\% vs. 0.6\%; p = 0.011). There was no significant correlation between the pre-hospital category of patients treated by EPs and the ED category. When a 12-lead-electrocardiogramm was recorded, the correlation improved slightly (rho: 0.139; p = 0.006).
    Conclusions: The percentage of ACS patients transported to hospital by an EP is very low, and EPs seem to be “over-aware” in the diagnosis of ACS.},
    number = {3},
    journal = {Resuscitation},
    author = {Gemes, Geza and Fuchs, Thomas J. and Wildner, Gernot and Smolle-Juettner, Freyja-Maria and Smolle, Josef and Stoschitzky, Kurt and Prause, Gerhard},
    year = {2005},
    pages = {323 -- 330}
}
Download Endnote/RIS citation
TY - JOUR
TI - The Acute Coronary Syndrome – Pre-Hospital Diagnostic Quality
AU - Gemes, Geza
AU - Fuchs, Thomas J.
AU - Wildner, Gernot
AU - Smolle-Juettner, Freyja-Maria
AU - Smolle, Josef
AU - Stoschitzky, Kurt
AU - Prause, Gerhard
T2 - Resuscitation
AB - Background and objective: In the Austrian emergency medical service (EMS), emergency medical technician-staffed and physician-staffed vehicles are in operation. Patients with suspected acute coronary syndromes (ACS) are treated in the pre-hospital phase and transported to the hospital by an emergency physician (EP). This study evaluates the diagnostic performance of EPs in ACS and the impact of this emergency system on the outcome of ACS in an urban area.
Design: Retrospective case control study.
Methods: All protocol sheets from the emergency physicians were searched for the diagnosis of ACS. The database of the emergency department (ED) was searched for patients with ACS as an admission diagnosis or ACS as discharge diagnosis. For patients admitted to an intensive care unit (ICU), the medical history from the ICU was reviewed. According to the diagnosis and the aggressiveness of therapy, patients were divided in five categories of severity at each stage of care (pre-hospital category, ED category, ICU category).
Results: A total of 3585 patients was analysed. Only 17.8% of the patients with ACS as the admission diagnosis and 20.3% of the patients with ACS as the discharge diagnosis were transported by an EP. 46.8% of the ACS diagnosis by EPs were confirmed in hospital. Patients transported by EPs showed a higher all-cause mortality in hospital (1.6% vs. 0.6%; p = 0.011). There was no significant correlation between the pre-hospital category of patients treated by EPs and the ED category. When a 12-lead-electrocardiogramm was recorded, the correlation improved slightly (rho: 0.139; p = 0.006).
Conclusions: The percentage of ACS patients transported to hospital by an EP is very low, and EPs seem to be “over-aware” in the diagnosis of ACS.
DA - 2005///
PY - 2005
DO - DOI: 10.1016/j.resuscitation.2005.04.006
VL - 66
IS - 3
SP - 323
EP - 330
SN - 0300-9572
UR - http://www.sciencedirect.com/science/article/B6T19-4GV9846-3/2/bcd3755266ba68b84536b39803426401
ER -
Background and objective: In the Austrian emergency medical service (EMS), emergency medical technician-staffed and physician-staffed vehicles are in operation. Patients with suspected acute coronary syndromes (ACS) are treated in the pre-hospital phase and transported to the hospital by an emergency physician (EP). This study evaluates the diagnostic performance of EPs in ACS and the impact of this emergency system on the outcome of ACS in an urban area. Design: Retrospective case control study. Methods: All protocol sheets from the emergency physicians were searched for the diagnosis of ACS. The database of the emergency department (ED) was searched for patients with ACS as an admission diagnosis or ACS as discharge diagnosis. For patients admitted to an intensive care unit (ICU), the medical history from the ICU was reviewed. According to the diagnosis and the aggressiveness of therapy, patients were divided in five categories of severity at each stage of care (pre-hospital category, ED category, ICU category). Results: A total of 3585 patients was analysed. Only 17.8% of the patients with ACS as the admission diagnosis and 20.3% of the patients with ACS as the discharge diagnosis were transported by an EP. 46.8% of the ACS diagnosis by EPs were confirmed in hospital. Patients transported by EPs showed a higher all-cause mortality in hospital (1.6% vs. 0.6%; p = 0.011). There was no significant correlation between the pre-hospital category of patients treated by EPs and the ED category. When a 12-lead-electrocardiogramm was recorded, the correlation improved slightly (rho: 0.139; p = 0.006). Conclusions: The percentage of ACS patients transported to hospital by an EP is very low, and EPs seem to be “over-aware” in the diagnosis of ACS.





_autopubs.cshtml:

Computational Pathology.
Peter J. Schüffler, Qing Zhong, Peter J. Wild and Thomas J. Fuchs.
In: Johannes Haybäck (ed.) Mechanisms of Molecular Carcinogenesis - Volume 2, 1st ed. 2017 edition, Springer, ISBN 3-319-53660-5, 21. Jun. 2017
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@incollection{schuffler_computational_2017,
    edition = {1st ed. 2017 edition},
    title = {Computational {Pathology}},
    isbn = {3-319-53660-5},
    url = {http://www.springer.com/de/book/9783319536606},
    booktitle = {Mechanisms of {Molecular} {Carcinogenesis} - {Volume} 2},
    publisher = {Springer},
    author = {Sch\"uffler, Peter J. and Zhong, Qing and Wild, Peter J. and Fuchs, Thomas J.},
    editor = {Hayb\"ack, Johannes},
    month = jun,
    year = {2017}
}
Download Endnote/RIS citation
TY - CHAP
TI - Computational Pathology
AU - Schüffler, Peter J.
AU - Zhong, Qing
AU - Wild, Peter J.
AU - Fuchs, Thomas J.
T2 - Mechanisms of Molecular Carcinogenesis - Volume 2
A2 - Haybäck, Johannes
DA - 2017/06/21/
PY - 2017
ET - 1st ed. 2017 edition
PB - Springer
SN - 3-319-53660-5
UR - http://www.springer.com/de/book/9783319536606
ER -
Scaling of cytoskeletal organization with cell size in Drosophila.
Alison K. Spencer, Andrew J. Schaumberg and Jennifer A. Zallen.
Mol. Biol. Cell, p. mbc.E16-10-0691, 2017-04-12
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@article{spencer_scaling_2017,
    title = {Scaling of cytoskeletal organization with cell size in {Drosophila}},
    issn = {1059-1524, 1939-4586},
    url = {http://www.molbiolcell.org/content/early/2017/04/10/mbc.E16-10-0691},
    doi = {10.1091/mbc.E16-10-0691},
    abstract = {Spatially organized macromolecular complexes are essential for cell and tissue function, but the mechanisms that organize micron-scale structures within cells are not well understood. Microtubule-based structures such as mitotic spindles scale with cell size, but less is known about the scaling of actin structures within cells. Actin-rich denticle precursors cover the ventral surface of the Drosophila embryo and larva and provide templates for cuticular structures involved in larval locomotion. Using quantitative imaging and statistical modeling, we demonstrate that denticle number and spacing scale with cell size over a wide range of cell lengths in embryos and larvae. Denticle number and spacing are reduced under space-limited conditions, and both features robustly scale over a ten-fold increase in cell length during larval growth. We show that the relationship between cell length and denticle spacing can be recapitulated by specific mathematical equations in embryos and larvae, and that accurate denticle spacing requires an intact microtubule network and the microtubule minus-end-binding protein, Patronin. These results identify a novel mechanism of microtubule-dependent actin scaling that maintains precise patterns of actin organization during tissue growth.},
    language = {en},
    urldate = {2017-04-24TZ},
    journal = {Molecular Biology of the Cell},
    author = {Spencer, Alison K. and Schaumberg, Andrew J. and Zallen, Jennifer A.},
    month = apr,
    year = {2017},
    pages = {mbc.E16--10--0691}
}
Download Endnote/RIS citation
TY - JOUR
TI - Scaling of cytoskeletal organization with cell size in Drosophila
AU - Spencer, Alison K.
AU - Schaumberg, Andrew J.
AU - Zallen, Jennifer A.
T2 - Molecular Biology of the Cell
AB - Spatially organized macromolecular complexes are essential for cell and tissue function, but the mechanisms that organize micron-scale structures within cells are not well understood. Microtubule-based structures such as mitotic spindles scale with cell size, but less is known about the scaling of actin structures within cells. Actin-rich denticle precursors cover the ventral surface of the Drosophila embryo and larva and provide templates for cuticular structures involved in larval locomotion. Using quantitative imaging and statistical modeling, we demonstrate that denticle number and spacing scale with cell size over a wide range of cell lengths in embryos and larvae. Denticle number and spacing are reduced under space-limited conditions, and both features robustly scale over a ten-fold increase in cell length during larval growth. We show that the relationship between cell length and denticle spacing can be recapitulated by specific mathematical equations in embryos and larvae, and that accurate denticle spacing requires an intact microtubule network and the microtubule minus-end-binding protein, Patronin. These results identify a novel mechanism of microtubule-dependent actin scaling that maintains precise patterns of actin organization during tissue growth.
DA - 2017/04/12/
PY - 2017
DO - 10.1091/mbc.E16-10-0691
DP - www.molbiolcell.org
SP - mbc.E16
EP - 10-0691
J2 - Mol. Biol. Cell
LA - en
SN - 1059-1524, 1939-4586
UR - http://www.molbiolcell.org/content/early/2017/04/10/mbc.E16-10-0691
Y2 - 2017/04/24/T16:19:42Z
ER -
Spatially organized macromolecular complexes are essential for cell and tissue function, but the mechanisms that organize micron-scale structures within cells are not well understood. Microtubule-based structures such as mitotic spindles scale with cell size, but less is known about the scaling of actin structures within cells. Actin-rich denticle precursors cover the ventral surface of the Drosophila embryo and larva and provide templates for cuticular structures involved in larval locomotion. Using quantitative imaging and statistical modeling, we demonstrate that denticle number and spacing scale with cell size over a wide range of cell lengths in embryos and larvae. Denticle number and spacing are reduced under space-limited conditions, and both features robustly scale over a ten-fold increase in cell length during larval growth. We show that the relationship between cell length and denticle spacing can be recapitulated by specific mathematical equations in embryos and larvae, and that accurate denticle spacing requires an intact microtubule network and the microtubule minus-end-binding protein, Patronin. These results identify a novel mechanism of microtubule-dependent actin scaling that maintains precise patterns of actin organization during tissue growth.
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations.
Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo and Thomas J. Fuchs.
Proceedings of the 1st Machine Learning for Healthcare Conference, Machine Learning for Healthcare, vol. 56, p. 191-208, Proceedings of Machine Learning Research, PMLR, 18 Aug 2016
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@inproceedings{schuffler_mitochondria-based_2016,
    address = {Los Angeles},
    series = {Proceedings of {Machine} {Learning} {Research}},
    title = {Mitochondria-based {Renal} {Cell} {Carcinoma} {Subtyping}: {Learning} from {Deep} vs. {Flat} {Feature} {Representations}},
    volume = {56},
    url = {http://proceedings.mlr.press/v56/Schuffler16.html},
    abstract = {Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
    Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
    In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
    The best model reaches a cross-validation accuracy of 89\%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.},
    language = {English},
    booktitle = {Proceedings of the 1st {Machine} {Learning} for {Healthcare} {Conference}},
    publisher = {PMLR},
    author = {Sch\"uffler, Peter J. and Sarungbam, Judy and Muhammad, Hassan and Reznik, Ed and Tickoo, Satish K. and Fuchs, Thomas J.},
    editor = {Finale, Doshi-Valez and Fackler, Jim and Kale, David and Wallace, Byron and Weins, Jenna},
    month = aug,
    year = {2016},
    pages = {191--208}
}
Download Endnote/RIS citation
TY - CONF
TI - Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations
AU - Schüffler, Peter J.
AU - Sarungbam, Judy
AU - Muhammad, Hassan
AU - Reznik, Ed
AU - Tickoo, Satish K.
AU - Fuchs, Thomas J.
T2 - Machine Learning for Healthcare
A2 - Finale, Doshi-Valez
A2 - Fackler, Jim
A2 - Kale, David
A2 - Wallace, Byron
A2 - Weins, Jenna
T3 - Proceedings of Machine Learning Research
AB - Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative.
Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification.
In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN).
The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
C1 - Los Angeles
C3 - Proceedings of the 1st Machine Learning for Healthcare Conference
DA - 2016/08/18/
PY - 2016
VL - 56
SP - 191
EP - 208
LA - English
PB - PMLR
UR - http://proceedings.mlr.press/v56/Schuffler16.html
ER -
Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions. New discoveries of significant mitochondria alterations between subtypes makes immunohistochemical (IHC) staining based image classification an imperative. Until now, accurate quantification and subtyping was made impossible by huge IHC variations, the absence of cell membrane staining for cytoplasm segmentation as well as the complete lack of systems for robust and reproducible image based classification. In this paper we present a comprehensive classification framework to overcome these challenges for tissue microarrays (TMA) of RCCs. We compare and evaluate models based on domain specific hand-crafted "flat"-features versus "deep" feature representations from various layers of a pre-trained convolutional neural network (CNN). The best model reaches a cross-validation accuracy of 89%, which demonstrates for the first time, that robust mitochondria-based subtyping of renal cancer is feasible.
A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes.
Stephanie R. Debats, Dee Luo, Lyndon D. Estes, Thomas J. Fuchs and Kelly K. Caylor.
Remote Sensing of Environment, vol. 179, p. 210-221, June 15, 2016
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{debats_generalized_2016,
    title = {A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes},
    volume = {179},
    issn = {0034-4257},
    url = {http://www.sciencedirect.com/science/article/pii/S0034425716301031},
    doi = {10.1016/j.rse.2016.03.010},
    abstract = {Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.},
    urldate = {2017-05-19TZ},
    journal = {Remote Sensing of Environment},
    author = {Debats, Stephanie R. and Luo, Dee and Estes, Lyndon D. and Fuchs, Thomas J. and Caylor, Kelly K.},
    month = jun,
    year = {2016},
    keywords = {Agriculture, Land cover, Sub-Saharan Africa, computer vision, machine learning},
    pages = {210--221}
}
Download Endnote/RIS citation
TY - JOUR
TI - A generalized computer vision approach to mapping crop fields in heterogeneous agricultural landscapes
AU - Debats, Stephanie R.
AU - Luo, Dee
AU - Estes, Lyndon D.
AU - Fuchs, Thomas J.
AU - Caylor, Kelly K.
T2 - Remote Sensing of Environment
AB - Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.
DA - 2016/06/15/
PY - 2016
DO - 10.1016/j.rse.2016.03.010
DP - ScienceDirect
VL - 179
SP - 210
EP - 221
J2 - Remote Sensing of Environment
SN - 0034-4257
UR - http://www.sciencedirect.com/science/article/pii/S0034425716301031
Y2 - 2017/05/19/T10:44:55Z
KW - Agriculture
KW - Land cover
KW - Sub-Saharan Africa
KW - computer vision
KW - machine learning
ER -
Smallholder farms dominate in many parts of the world, particularly Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring a specialized methodology to map agricultural land cover. Using a variety of sites in South Africa, we present a new approach to mapping agricultural fields, based on efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. We achieved similar high performance across agricultural types, including the spectrally indistinct smallholder fields as well as the more easily distinguishable commercial fields, and demonstrated the ability to generalize performance across large geographic areas. In sensitivity analyses, we determined multi-temporal information provided greater gains in performance than the addition of multi-spectral bands available in DigitalGlobe Worldview-2 imagery.
Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
Jakob M. Burgstaller, Peter J. Schüffler, Joachim M. Buhmann, Gustav Andreisek, Sebastian Winklhofer, Filippo Del Grande, Michèle Mattle, Florian Brunner, Georgios Karakoumis, Johann Steurer and Ulrike Held.
Spine, vol. 41, 17, p. 1053-1062, 2016
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@article{burgstaller_is_2016,
    title = {Is {There} an {Association} {Between} {Pain} and {Magnetic} {Resonance} {Imaging} {Parameters} in {Patients} {With} {Lumbar} {Spinal} {Stenosis}?},
    volume = {41},
    issn = {0362-2436},
    shorttitle = {Is {There} an {Association} {Between} {Pain} and {Magnetic} {Resonance} {Imaging} {Parameters} in {Patients} {With} {Lumbar} {Spinal} {Stenosis}?},
    url = {http://Insights.ovid.com/crossref?an=00007632-201609010-00015},
    doi = {10.1097/BRS.0000000000001544},
    abstract = {STUDY DESIGN:
    A prospective multicenter cohort study.
    OBJECTIVE:
    The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS).
    SUMMARY OF BACKGROUND DATA:
    At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear.
    METHODS:
    First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS).
    RESULTS:
    In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown.
    CONCLUSION:
    Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints.
    LEVEL OF EVIDENCE:
    2.},
    language = {en},
    number = {17},
    urldate = {2017-02-11TZ},
    journal = {Spine},
    author = {Burgstaller, Jakob M. and Sch\"uffler, Peter J. and Buhmann, Joachim M. and Andreisek, Gustav and Winklhofer, Sebastian and Del Grande, Filippo and Mattle, Mich\`ele and Brunner, Florian and Karakoumis, Georgios and Steurer, Johann and Held, Ulrike},
    year = {2016},
    pages = {1053--1062}
}
Download Endnote/RIS citation
TY - JOUR
TI - Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
AU - Burgstaller, Jakob M.
AU - Schüffler, Peter J.
AU - Buhmann, Joachim M.
AU - Andreisek, Gustav
AU - Winklhofer, Sebastian
AU - Del Grande, Filippo
AU - Mattle, Michèle
AU - Brunner, Florian
AU - Karakoumis, Georgios
AU - Steurer, Johann
AU - Held, Ulrike
T2 - Spine
AB - STUDY DESIGN:
A prospective multicenter cohort study.
OBJECTIVE:
The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS).
SUMMARY OF BACKGROUND DATA:
At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear.
METHODS:
First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS).
RESULTS:
In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown.
CONCLUSION:
Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints.
LEVEL OF EVIDENCE:
2.
DA - 2016///
PY - 2016
DO - 10.1097/BRS.0000000000001544
DP - CrossRef
VL - 41
IS - 17
SP - 1053
EP - 1062
LA - en
SN - 0362-2436
ST - Is There an Association Between Pain and Magnetic Resonance Imaging Parameters in Patients With Lumbar Spinal Stenosis?
UR - http://Insights.ovid.com/crossref?an=00007632-201609010-00015
Y2 - 2017/02/11/T00:39:09Z
ER -
STUDY DESIGN: A prospective multicenter cohort study. OBJECTIVE: The aim of this study was to identify an association between pain and magnetic resonance imaging (MRI) parameters in patients with lumbar spinal stenosis (LSS). SUMMARY OF BACKGROUND DATA: At present, the relationship between abnormal MRI findings and pain in patients with LSS is still unclear. METHODS: First, we conducted a systematic literature search. We identified relationships of relevant MRI parameters and pain in patients with LSS. Second, we addressed the study question with a thorough descriptive and graphical analysis to establish a relationship between MRI parameters and pain using data of the LSS outcome study (LSOS). RESULTS: In the systematic review including four papers about the associations between radiological findings in the MRI and pain, the authors of two articles reported no association and two of them did. Of the latters, only one study found a moderate correlation between leg pain measured by Visual Analog Scale (VAS) and the degree of stenosis assessed by spine surgeons. In the data of the LSOS study, we could not identify a relevant association between any of the MRI parameters and buttock, leg, and back pain, quantified by the Spinal Stenosis Measure (SSM) and the Numeric Rating Scale (NRS). Even by restricting the analysis to the level of the lumbar spine with the most prominent radiological "stenosis," no relevant association could be shown. CONCLUSION: Despite a thorough analysis of the data, we were not able to prove any correlation between radiological findings (MRI) and the severity of pain. There is a need for innovative "methods/techniques" to learn more about the causal relationship between radiological findings and the patients' pain-related complaints. LEVEL OF EVIDENCE: 2.
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope.
Andrew J. Schaumberg, S. Joseph Sirintrapun, Hikmat A. Al-Ahmadie, Peter J. Schüffler and Thomas J. Fuchs.
13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics, CIBB, 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schaumberg_deepscope:_2016,
    address = {Stirling, United Kingdom},
    title = {{DeepScope}: {Nonintrusive} {Whole} {Slide} {Saliency} {Annotation} and {Prediction} from {Pathologists} at the {Microscope}},
    shorttitle = {{DeepScope}},
    url = {http://www.cs.stir.ac.uk/events/cibb2016/},
    abstract = {Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks.
    Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image.
    We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide.
    Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15\% in bladder and 91.50\% in prostate, with 75.00\% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.},
    language = {English},
    booktitle = {13th {International} {Conference} on {Computational} {Intelligence} methods for {Bioinformatics} and {Biostatistics}},
    author = {Schaumberg, Andrew J. and Sirintrapun, S. Joseph and Al-Ahmadie, Hikmat A. and Sch\"uffler, Peter J. and Fuchs, Thomas J.},
    year = {2016},
}
Download Endnote/RIS citation
TY - CONF
TI - DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope
AU - Schaumberg, Andrew J.
AU - Sirintrapun, S. Joseph
AU - Al-Ahmadie, Hikmat A.
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
T2 - CIBB
AB - Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks.
Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image.
We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide.
Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.
C1 - Stirling, United Kingdom
C3 - 13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
DA - 2016///
PY - 2016
LA - English
ST - DeepScope
UR - http://www.cs.stir.ac.uk/events/cibb2016/
ER -
Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks. Unfortunately, most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration's ruling that primary diagnosis must come from a glass slide rather than a digital image. We present the first end-to-end framework to overcome this problem, gathering annotations in a nonintrusive manner during a pathologist's routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observing time are estimated to generate a spatial and temporal saliency map of the whole slide. Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.
Big Data in der Medizin.
Qing Zhong, Roman Barnert, Gunnar Ratsch, Thomas J. Fuchs and Peter J. Wild.
Leading Opinions: Hämatologie & Onkologie, p. 102-105, 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{zhong_big_2016,
    title = {Big {Data} in der {Medizin}},
    abstract = {IT-Systeme in Krankenh\"ausern bereiten Unmengen von Daten aus unterschiedlichen Quellen, die von spezialisierten interdisziplin\"aren Teams gewonnen worden sind, zu wertvollen Informationen auf. Durch die Ausweitung der Analysen auf mehrere Tausend anonymisierte Patienten und die Vernetzung mit anderen Kliniken und Forschungszentren wird ein \"Okosystem zum Wohle des Patienten geschaffen. Dieses auf Big-Data-Ans\"atzen gest\"utzte, evidenzbasierte und patientenorientierte Gesundheitswesen ist in naher Zukunft Realit\"at.},
    language = {German},
    journal = {Leading Opinions: H\"amatologie \& Onkologie},
    author = {Zhong, Qing and Barnert, Roman and Ratsch, Gunnar and Fuchs, Thomas J. and Wild, Peter J.},
    year = {2016},
    pages = {102--105}
}
Download Endnote/RIS citation
TY - NEWS
TI - Big Data in der Medizin
AU - Zhong, Qing
AU - Barnert, Roman
AU - Ratsch, Gunnar
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
T2 - Leading Opinions: Hämatologie & Onkologie
AB - IT-Systeme in Krankenhäusern bereiten Unmengen von Daten aus unterschiedlichen Quellen, die von spezialisierten interdisziplinären Teams gewonnen worden sind, zu wertvollen Informationen auf. Durch die Ausweitung der Analysen auf mehrere Tausend anonymisierte Patienten und die Vernetzung mit anderen Kliniken und Forschungszentren wird ein Ökosystem zum Wohle des Patienten geschaffen. Dieses auf Big-Data-Ansätzen gestützte, evidenzbasierte und patientenorientierte Gesundheitswesen ist in naher Zukunft Realität.
DA - 2016///
PY - 2016
SP - 102
EP - 105
LA - German
ER -
IT-Systeme in Krankenhäusern bereiten Unmengen von Daten aus unterschiedlichen Quellen, die von spezialisierten interdisziplinären Teams gewonnen worden sind, zu wertvollen Informationen auf. Durch die Ausweitung der Analysen auf mehrere Tausend anonymisierte Patienten und die Vernetzung mit anderen Kliniken und Forschungszentren wird ein Ökosystem zum Wohle des Patienten geschaffen. Dieses auf Big-Data-Ansätzen gestützte, evidenzbasierte und patientenorientierte Gesundheitswesen ist in naher Zukunft Realität.
Cancer-secreted AGR2 induces programmed cell death in normal cells.
Elizabeth A. Vitello, Sue-Ing Quek, Heather Kincaid, Thomas Fuchs, Daniel J. Crichton, Pamela Troisch and Alvin Y. Liu.
Oncotarget, vol. 5, 0, 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{vitello_cancer-secreted_2016,
    title = {Cancer-secreted {AGR}2 induces programmed cell death in normal cells},
    volume = {5},
    issn = {1949-2553},
    url = {http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget&page=article&op=view&path%5B%5D=9921},
    abstract = {Anterior Gradient 2 (AGR2) is a protein expressed in many solid tumor types including prostate, pancreatic, breast and lung. AGR2 functions as a protein disulfide isomerase in the endoplasmic reticulum. However, AGR2 is secreted by cancer cells that overexpress this molecule. Secretion of AGR2 was also found in salamander limb regeneration. Due to its ubiquity, tumor secretion of AGR2 must serve an important role in cancer, yet its molecular function is largely unknown. This study examined the effect of cancer-secreted AGR2 on normal cells. Prostate stromal cells were cultured, and tissue digestion media containing AGR2 prepared from prostate primary cancer 10-076 CP and adenocarcinoma LuCaP 70CR xenograft were added. The control were tissue digestion media containing no AGR2 prepared from benign prostate 10-076 NP and small cell carcinoma LuCaP 145.1 xenograft. In the presence of tumor-secreted AGR2, the stromal cells were found to undergo programmed cell death (PCD) characterized by formation of cellular blebs, cell shrinkage, and DNA fragmentation as seen when the stromal cells were UV irradiated or treated by a pro-apoptotic drug. PCD could be prevented with the addition of the monoclonal AGR2-neutralizing antibody P3A5. DNA microarray analysis of LuCaP 70CR media-treated vs . LuCaP 145.1 media-treated cells showed downregulation of the gene SAT1 as a major change in cells exposed to AGR2. RT-PCR analysis confirmed the array result. SAT1 encodes spermidine/spermine N 1 -acetyltransferase, which maintains intracellular polyamine levels. Abnormal polyamine metabolism as a result of altered SAT1 activity has an adverse effect on cells through the induction of PCD.},
    number = {0},
    urldate = {2016-06-16TZ},
    journal = {Oncotarget},
    author = {Vitello, Elizabeth A. and Quek, Sue-Ing and Kincaid, Heather and Fuchs, Thomas and Crichton, Daniel J. and Troisch, Pamela and Liu, Alvin Y.},
    year = {2016},
}
Download Endnote/RIS citation
TY - JOUR
TI - Cancer-secreted AGR2 induces programmed cell death in normal cells
AU - Vitello, Elizabeth A.
AU - Quek, Sue-Ing
AU - Kincaid, Heather
AU - Fuchs, Thomas
AU - Crichton, Daniel J.
AU - Troisch, Pamela
AU - Liu, Alvin Y.
T2 - Oncotarget
AB - Anterior Gradient 2 (AGR2) is a protein expressed in many solid tumor types including prostate, pancreatic, breast and lung. AGR2 functions as a protein disulfide isomerase in the endoplasmic reticulum. However, AGR2 is secreted by cancer cells that overexpress this molecule. Secretion of AGR2 was also found in salamander limb regeneration. Due to its ubiquity, tumor secretion of AGR2 must serve an important role in cancer, yet its molecular function is largely unknown. This study examined the effect of cancer-secreted AGR2 on normal cells. Prostate stromal cells were cultured, and tissue digestion media containing AGR2 prepared from prostate primary cancer 10-076 CP and adenocarcinoma LuCaP 70CR xenograft were added. The control were tissue digestion media containing no AGR2 prepared from benign prostate 10-076 NP and small cell carcinoma LuCaP 145.1 xenograft. In the presence of tumor-secreted AGR2, the stromal cells were found to undergo programmed cell death (PCD) characterized by formation of cellular blebs, cell shrinkage, and DNA fragmentation as seen when the stromal cells were UV irradiated or treated by a pro-apoptotic drug. PCD could be prevented with the addition of the monoclonal AGR2-neutralizing antibody P3A5. DNA microarray analysis of LuCaP 70CR media-treated vs . LuCaP 145.1 media-treated cells showed downregulation of the gene SAT1 as a major change in cells exposed to AGR2. RT-PCR analysis confirmed the array result. SAT1 encodes spermidine/spermine N 1 -acetyltransferase, which maintains intracellular polyamine levels. Abnormal polyamine metabolism as a result of altered SAT1 activity has an adverse effect on cells through the induction of PCD.
DA - 2016///
PY - 2016
DP - www.impactjournals.com
VL - 5
IS - 0
SN - 1949-2553
UR - http://www.impactjournals.com/oncotarget/index.php?journal=oncotarget&page=article&op=view&path%5B%5D=9921
Y2 - 2016/06/16/T02:00:19Z
ER -
Anterior Gradient 2 (AGR2) is a protein expressed in many solid tumor types including prostate, pancreatic, breast and lung. AGR2 functions as a protein disulfide isomerase in the endoplasmic reticulum. However, AGR2 is secreted by cancer cells that overexpress this molecule. Secretion of AGR2 was also found in salamander limb regeneration. Due to its ubiquity, tumor secretion of AGR2 must serve an important role in cancer, yet its molecular function is largely unknown. This study examined the effect of cancer-secreted AGR2 on normal cells. Prostate stromal cells were cultured, and tissue digestion media containing AGR2 prepared from prostate primary cancer 10-076 CP and adenocarcinoma LuCaP 70CR xenograft were added. The control were tissue digestion media containing no AGR2 prepared from benign prostate 10-076 NP and small cell carcinoma LuCaP 145.1 xenograft. In the presence of tumor-secreted AGR2, the stromal cells were found to undergo programmed cell death (PCD) characterized by formation of cellular blebs, cell shrinkage, and DNA fragmentation as seen when the stromal cells were UV irradiated or treated by a pro-apoptotic drug. PCD could be prevented with the addition of the monoclonal AGR2-neutralizing antibody P3A5. DNA microarray analysis of LuCaP 70CR media-treated vs . LuCaP 145.1 media-treated cells showed downregulation of the gene SAT1 as a major change in cells exposed to AGR2. RT-PCR analysis confirmed the array result. SAT1 encodes spermidine/spermine N 1 -acetyltransferase, which maintains intracellular polyamine levels. Abnormal polyamine metabolism as a result of altered SAT1 activity has an adverse effect on cells through the induction of PCD.
Multi-Organ Cancer Classification and Survival Analysis.
Stefan Bauer, Nicolas Carion, Peter Schüffler, Thomas Fuchs, Peter Wild and Joachim M. Buhmann.
arXiv:1606.00897 [cs, q-bio, stat], 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{bauer_multi-organ_2016,
    title = {Multi-{Organ} {Cancer} {Classification} and {Survival} {Analysis}},
    url = {http://arxiv.org/abs/1606.00897},
    abstract = {Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist (\$p=0.006\$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.},
    urldate = {2016-06-16TZ},
    journal = {arXiv:1606.00897 [cs, q-bio, stat]},
    author = {Bauer, Stefan and Carion, Nicolas and Sch\"uffler, Peter and Fuchs, Thomas and Wild, Peter and Buhmann, Joachim M.},
    year = {2016},
    keywords = {Computer Science - Learning, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs, Statistics - Machine Learning}
}
Download Endnote/RIS citation
TY - JOUR
TI - Multi-Organ Cancer Classification and Survival Analysis
AU - Bauer, Stefan
AU - Carion, Nicolas
AU - Schüffler, Peter
AU - Fuchs, Thomas
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - arXiv:1606.00897 [cs, q-bio, stat]
AB - Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist ($p=0.006$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.
DA - 2016///
PY - 2016
DP - arXiv.org
UR - http://arxiv.org/abs/1606.00897
Y2 - 2016/06/16/T01:52:59Z
KW - Computer Science - Learning
KW - Quantitative Biology - Quantitative Methods
KW - Quantitative Biology - Tissues and Organs
KW - Statistics - Machine Learning
ER -
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between datasets and organ sites. In this paper we implement and evaluate a variety of deep neural network models and model ensembles for nuclei classification in renal cell cancer (RCC) and prostate cancer (PCa). We propose a convolutional neural network system based on residual learning which significantly improves over the state-of-the-art in cell nuclei classification. However, the main thrust of our work is to demonstrate for the first time the models ability to transfer the learned concepts from one organ type to another, with robust performance. Finally, we show that the combination of tissue types during training increases not only classification accuracy but also overall survival analysis. The best model, trained on combined data of RCC and PCa, exhibits optimal performance on PCa classification and better survival group stratification than an expert pathologist ($p=0.006$). All code, image data and expert labels are made publicly available to serve as benchmark for the community for future research into computational pathology.
Classifying Renal Cell Carcinoma by Using Convolutional Neural Networks to Deconstruct Pathological Images.
Hassan Muhammad, Peter J. Schüffler, Judy Sarungbam, Satish K. Tickoo and Thomas Fuchs.
Vincent du Vigneaud Memorial Research Symposium, 2016
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@misc{muhammad_classifying_2016,
    address = {Weill Cornell Medicine},
    type = {Poster},
    title = {Classifying {Renal} {Cell} {Carcinoma} by {Using} {Convolutional} {Neural} {Networks} to {Deconstruct} {Pathological} {Images}.},
    author = {Muhammad, Hassan},
    collaborator = {Sch\"uffler, Peter J. and Sarungbam, Judy and Tickoo, Satish K. and Fuchs, Thomas},
    year = {2016},
}
Download Endnote/RIS citation
TY - SLIDE
TI - Classifying Renal Cell Carcinoma by Using Convolutional Neural Networks to Deconstruct Pathological Images.
T2 - Vincent du Vigneaud Memorial Research Symposium
A2 - Muhammad, Hassan
CY - Weill Cornell Medicine
DA - 2016///
PY - 2016
M3 - Poster
ER -
Image-Based Computational Quantification and Visualization of Genetic Alterations and Tumour Heterogeneity.
Qing Zhong, Jan H. Rüschoff, Tiannan Guo, Maria Gabrani, Peter J. Schüffler, Markus Rechsteiner, Yansheng Liu, Thomas J. Fuchs, Niels J. Rupp, Christian Fankhauser, Joachim M. Buhmann, Sven Perner, Cédric Poyet, Miriam Blattner, Davide Soldini, Holger Moch, Mark A. Rubin, Aurelia Noske, Josef Rüschoff, Michael C. Haffner, Wolfram Jochum and Peter J. Wild.
Scientific Reports, vol. 6, p. 24146, 2016
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@article{zhong_image-based_2016,
    title = {Image-{Based} {Computational} {Quantification} and {Visualization} of {Genetic} {Alterations} and {Tumour} {Heterogeneity}},
    volume = {6},
    issn = {2045-2322},
    url = {http://www.nature.com/articles/srep24146},
    doi = {10.1038/srep24146},
    urldate = {2016-04-12TZ},
    journal = {Scientific Reports},
    author = {Zhong, Qing and R\"uschoff, Jan H. and Guo, Tiannan and Gabrani, Maria and Sch\"uffler, Peter J. and Rechsteiner, Markus and Liu, Yansheng and Fuchs, Thomas J. and Rupp, Niels J. and Fankhauser, Christian and Buhmann, Joachim M. and Perner, Sven and Poyet, C\'edric and Blattner, Miriam and Soldini, Davide and Moch, Holger and Rubin, Mark A. and Noske, Aurelia and R\"uschoff, Josef and Haffner, Michael C. and Jochum, Wolfram and Wild, Peter J.},
    year = {2016},
    pages = {24146}
}
Download Endnote/RIS citation
TY - JOUR
TI - Image-Based Computational Quantification and Visualization of Genetic Alterations and Tumour Heterogeneity
AU - Zhong, Qing
AU - Rüschoff, Jan H.
AU - Guo, Tiannan
AU - Gabrani, Maria
AU - Schüffler, Peter J.
AU - Rechsteiner, Markus
AU - Liu, Yansheng
AU - Fuchs, Thomas J.
AU - Rupp, Niels J.
AU - Fankhauser, Christian
AU - Buhmann, Joachim M.
AU - Perner, Sven
AU - Poyet, Cédric
AU - Blattner, Miriam
AU - Soldini, Davide
AU - Moch, Holger
AU - Rubin, Mark A.
AU - Noske, Aurelia
AU - Rüschoff, Josef
AU - Haffner, Michael C.
AU - Jochum, Wolfram
AU - Wild, Peter J.
T2 - Scientific Reports
DA - 2016///
PY - 2016
DO - 10.1038/srep24146
DP - CrossRef
VL - 6
SP - 24146
SN - 2045-2322
UR - http://www.nature.com/articles/srep24146
Y2 - 2016/04/12/T01:49:30Z
ER -
Autonomous Terrain Classification With Co- and Self-Training Approach.
K. Otsu, M. Ono, T. J. Fuchs, I. Baldwin and T. Kubota.
IEEE Robotics and Automation Letters, vol. 1, 2, p. 814-819, 2016
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@article{otsu_autonomous_2016,
    title = {Autonomous {Terrain} {Classification} {With} {Co}- and {Self}-{Training} {Approach}},
    volume = {1},
    issn = {2377-3766},
    url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7397920},
    doi = {10.1109/LRA.2016.2525040},
    abstract = {Identifying terrain type is crucial to safely operating planetary exploration rovers. Vision-based terrain classifiers, which are typically trained by thousands of labeled images using machine learning methods, have proven to be particularly successful. However, since planetary rovers are to boldly go where no one has gone before, training data are usually not available a priori; instead, rovers have to quickly learn from their own experiences in an early phase of surface operation. This research addresses the challenge by combining two key ideas. The first idea is to use both onboard imagery and vibration data, and let rovers learn from physical experiences through self-supervised learning. The underlying fact is that visually similar terrain may be disambiguated by mechanical vibrations. The second idea is to employ the co- and self-training approaches. The idea of co-training is to train two classifiers separately for vision and vibration data, and re-train them iteratively on each other's output. Meanwhile, the self-training approach, applied only to the vision-based classifier, re-trains the classifier on its own output. Both approaches essentially increase the amount of labels, hence enable the terrain classifiers to operate from a sparse training dataset. The proposed approach was validated with a four-wheeled test rover in Mars-analogous terrain, including bedrock, soil, and sand. The co-training setup based on support vector machines with color and wavelet-based features successfully estimated terrain types with 82\% accuracy with only three labeled images.},
    number = {2},
    journal = {IEEE Robotics and Automation Letters},
    author = {Otsu, K. and Ono, M. and Fuchs, T. J. and Baldwin, I. and Kubota, T.},
    year = {2016},
    keywords = {Image color analysis, Mars, Mars-analogous terrain, Semantic Scene Understanding, Soil, Space Robotics, Support vector machines, Training, Training data, Visual Learning, Wheels, aerospace computing, autonomous terrain classification, co-training, color features, four-wheeled test rover, image classification, image colour analysis, learning (artificial intelligence), mechanical vibrations, onboard imagery, planetary rovers, self-supervised learning, self-training, terrain mapping, vibration data, vibrations, vision-based classifier, wavelet transforms, wavelet-based features},
    pages = {814--819}
}
Download Endnote/RIS citation
TY - JOUR
TI - Autonomous Terrain Classification With Co- and Self-Training Approach
AU - Otsu, K.
AU - Ono, M.
AU - Fuchs, T. J.
AU - Baldwin, I.
AU - Kubota, T.
T2 - IEEE Robotics and Automation Letters
AB - Identifying terrain type is crucial to safely operating planetary exploration rovers. Vision-based terrain classifiers, which are typically trained by thousands of labeled images using machine learning methods, have proven to be particularly successful. However, since planetary rovers are to boldly go where no one has gone before, training data are usually not available a priori; instead, rovers have to quickly learn from their own experiences in an early phase of surface operation. This research addresses the challenge by combining two key ideas. The first idea is to use both onboard imagery and vibration data, and let rovers learn from physical experiences through self-supervised learning. The underlying fact is that visually similar terrain may be disambiguated by mechanical vibrations. The second idea is to employ the co- and self-training approaches. The idea of co-training is to train two classifiers separately for vision and vibration data, and re-train them iteratively on each other's output. Meanwhile, the self-training approach, applied only to the vision-based classifier, re-trains the classifier on its own output. Both approaches essentially increase the amount of labels, hence enable the terrain classifiers to operate from a sparse training dataset. The proposed approach was validated with a four-wheeled test rover in Mars-analogous terrain, including bedrock, soil, and sand. The co-training setup based on support vector machines with color and wavelet-based features successfully estimated terrain types with 82% accuracy with only three labeled images.
DA - 2016///
PY - 2016
DO - 10.1109/LRA.2016.2525040
DP - IEEE Xplore
VL - 1
IS - 2
SP - 814
EP - 819
SN - 2377-3766
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7397920
KW - Image color analysis
KW - Mars
KW - Mars-analogous terrain
KW - Semantic Scene Understanding
KW - Soil
KW - Space Robotics
KW - Support vector machines
KW - Training
KW - Training data
KW - Visual Learning
KW - Wheels
KW - aerospace computing
KW - autonomous terrain classification
KW - co-training
KW - color features
KW - four-wheeled test rover
KW - image classification
KW - image colour analysis
KW - learning (artificial intelligence)
KW - mechanical vibrations
KW - onboard imagery
KW - planetary rovers
KW - self-supervised learning
KW - self-training
KW - terrain mapping
KW - vibration data
KW - vibrations
KW - vision-based classifier
KW - wavelet transforms
KW - wavelet-based features
ER -
Identifying terrain type is crucial to safely operating planetary exploration rovers. Vision-based terrain classifiers, which are typically trained by thousands of labeled images using machine learning methods, have proven to be particularly successful. However, since planetary rovers are to boldly go where no one has gone before, training data are usually not available a priori; instead, rovers have to quickly learn from their own experiences in an early phase of surface operation. This research addresses the challenge by combining two key ideas. The first idea is to use both onboard imagery and vibration data, and let rovers learn from physical experiences through self-supervised learning. The underlying fact is that visually similar terrain may be disambiguated by mechanical vibrations. The second idea is to employ the co- and self-training approaches. The idea of co-training is to train two classifiers separately for vision and vibration data, and re-train them iteratively on each other's output. Meanwhile, the self-training approach, applied only to the vision-based classifier, re-trains the classifier on its own output. Both approaches essentially increase the amount of labels, hence enable the terrain classifiers to operate from a sparse training dataset. The proposed approach was validated with a four-wheeled test rover in Mars-analogous terrain, including bedrock, soil, and sand. The co-training setup based on support vector machines with color and wavelet-based features successfully estimated terrain types with 82% accuracy with only three labeled images.
Predicting Drug Interactions and Mutagenicity with Ensemble Classifiers on Subgraphs of Molecules.
Andrew Schaumberg, Angela Yu, Tatsuhiro Koshi, Xiaochan Zong and Santoshkalyan Rayadhurgam.
arXiv:1601.07233 [cs, stat], 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{schaumberg_predicting_2016,
    title = {Predicting {Drug} {Interactions} and {Mutagenicity} with {Ensemble} {Classifiers} on {Subgraphs} of {Molecules}},
    url = {http://arxiv.org/abs/1601.07233},
    abstract = {In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this problem for their generality to all types of molecules, due to the inherent association of atoms through atomic bonds. Subgraphs can represent different molecular domains. These domains can be biologically significant as most molecules only have portions that are of functional significance and can interact with other domains. Thus, we use subgraphs as features in different machine learning algorithms to predict if two drugs interact and predict potential single molecule effects.},
    urldate = {2016-01-28TZ},
    journal = {arXiv:1601.07233 [cs, stat]},
    author = {Schaumberg, Andrew and Yu, Angela and Koshi, Tatsuhiro and Zong, Xiaochan and Rayadhurgam, Santoshkalyan},
    year = {2016},
    keywords = {Computer Science - Learning, I.2.1, J.3, Statistics - Machine Learning}
}
Download Endnote/RIS citation
TY - JOUR
TI - Predicting Drug Interactions and Mutagenicity with Ensemble Classifiers on Subgraphs of Molecules
AU - Schaumberg, Andrew
AU - Yu, Angela
AU - Koshi, Tatsuhiro
AU - Zong, Xiaochan
AU - Rayadhurgam, Santoshkalyan
T2 - arXiv:1601.07233 [cs, stat]
AB - In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this problem for their generality to all types of molecules, due to the inherent association of atoms through atomic bonds. Subgraphs can represent different molecular domains. These domains can be biologically significant as most molecules only have portions that are of functional significance and can interact with other domains. Thus, we use subgraphs as features in different machine learning algorithms to predict if two drugs interact and predict potential single molecule effects.
DA - 2016///
PY - 2016
DP - arXiv.org
UR - http://arxiv.org/abs/1601.07233
Y2 - 2016/01/28/T19:24:38Z
KW - Computer Science - Learning
KW - I.2.1
KW - J.3
KW - Statistics - Machine Learning
ER -
In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this problem for their generality to all types of molecules, due to the inherent association of atoms through atomic bonds. Subgraphs can represent different molecular domains. These domains can be biologically significant as most molecules only have portions that are of functional significance and can interact with other domains. Thus, we use subgraphs as features in different machine learning algorithms to predict if two drugs interact and predict potential single molecule effects.
Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys.
S. G. Djorgovski, M. J. Graham, C. Donalek, A. A. Mahabal, A. J. Drake, M. Turmon and T.J. Fuchs.
Future Generation Computer Systems, 2016
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{djorgovski_real-time_2016,
    title = {Real-{Time} {Data} {Mining} of {Massive} {Data} {Streams} from {Synoptic} {Sky} {Surveys}},
    issn = {0167-739X},
    url = {http://www.sciencedirect.com/science/article/pii/S0167739X1500326X},
    doi = {10.1016/j.future.2015.10.013},
    abstract = {The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.},
    urldate = {2016-01-20TZ},
    journal = {Future Generation Computer Systems},
    author = {Djorgovski, S. G. and Graham, M. J. and Donalek, C. and Mahabal, A. A. and Drake, A. J. and Turmon, M. and Fuchs, T.J.},
    year = {2016},
    keywords = {Automated decision making, Bayesian methods, Massive data streams, Sky surveys, machine learning}
}
Download Endnote/RIS citation
TY - JOUR
TI - Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys
AU - Djorgovski, S. G.
AU - Graham, M. J.
AU - Donalek, C.
AU - Mahabal, A. A.
AU - Drake, A. J.
AU - Turmon, M.
AU - Fuchs, T.J.
T2 - Future Generation Computer Systems
AB - The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
DA - 2016///
PY - 2016
DO - 10.1016/j.future.2015.10.013
DP - ScienceDirect
J2 - Future Generation Computer Systems
SN - 0167-739X
UR - http://www.sciencedirect.com/science/article/pii/S0167739X1500326X
Y2 - 2016/01/20/T14:32:18Z
KW - Automated decision making
KW - Bayesian methods
KW - Massive data streams
KW - Sky surveys
KW - machine learning
ER -
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
Automatic single cell segmentation on highly multiplexed tissue images.
Peter J. Schüffler, Denis Schapiro, Charlotte Giesen, Hao A. O. Wang, Bernd Bodenmiller and Joachim M. Buhmann.
Cytometry Part A, vol. 87, 10, p. 936-942, 10/2015
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_automatic_2015,
    title = {Automatic single cell segmentation on highly multiplexed tissue images},
    volume = {87},
    issn = {15524922},
    shorttitle = {Automatic single cell segmentation on highly multiplexed tissue images},
    url = {http://doi.wiley.com/10.1002/cyto.a.22702},
    doi = {10.1002/cyto.a.22702},
    abstract = {The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.},
    language = {en},
    number = {10},
    urldate = {2017-02-14TZ},
    journal = {Cytometry Part A},
    author = {Sch\"uffler, Peter J. and Schapiro, Denis and Giesen, Charlotte and Wang, Hao A. O. and Bodenmiller, Bernd and Buhmann, Joachim M.},
    month = oct,
    year = {2015},
    pages = {936--942}
}
Download Endnote/RIS citation
TY - JOUR
TI - Automatic single cell segmentation on highly multiplexed tissue images
AU - Schüffler, Peter J.
AU - Schapiro, Denis
AU - Giesen, Charlotte
AU - Wang, Hao A. O.
AU - Bodenmiller, Bernd
AU - Buhmann, Joachim M.
T2 - Cytometry Part A
AB - The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.
DA - 2015/10//
PY - 2015
DO - 10.1002/cyto.a.22702
DP - CrossRef
VL - 87
IS - 10
SP - 936
EP - 942
LA - en
SN - 15524922
ST - Automatic single cell segmentation on highly multiplexed tissue images
UR - http://doi.wiley.com/10.1002/cyto.a.22702
Y2 - 2017/02/14/T19:42:24Z
ER -
The combination of mass cytometry and immunohistochemistry (IHC) enables new histopathological imaging methods in which dozens of proteins and protein modifications can be visualized simultaneously in a single tissue section. The power of multiplexing combined with spatial information and quantification was recently illustrated on breast cancer tissue and was described as next-generation IHC. Robust, accurate, and high-throughput cell segmentation is crucial for the analysis of this new generation of IHC data. To this end, we propose a watershed-based cell segmentation, which uses a nuclear marker and multiple membrane markers, the latter automatically selected based on their correlation. In comparison with the state-of-the-art segmentation pipelines, which are only using a single marker for object detection, we could show that the use of multiple markers can significantly increase the segmentation power, and thus, multiplexed information should be used and not ignored during the segmentation. Furthermore, we provide a novel, user-friendly open-source toolbox for the automatic segmentation of multiplexed histopathological images. (c) 2015 International Society for Advancement of Cytometry.
Understanding Neural Networks Through Deep Visualization.
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs and Hod Lipson.
ICML Deep Learning Workshop, 2015
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{yosinski_understanding_2015,
    title = {Understanding {Neural} {Networks} {Through} {Deep} {Visualization}},
    url = {http://arxiv.org/abs/1506.06579},
    abstract = {Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup.},
    urldate = {2015-12-03TZ},
    booktitle = {{ICML} {Deep} {Learning} {Workshop}},
    author = {Yosinski, Jason and Clune, Jeff and Nguyen, Anh and Fuchs, Thomas and Lipson, Hod},
    year = {2015},
}
Download Endnote/RIS citation
TY - CONF
TI - Understanding Neural Networks Through Deep Visualization
AU - Yosinski, Jason
AU - Clune, Jeff
AU - Nguyen, Anh
AU - Fuchs, Thomas
AU - Lipson, Hod
AB - Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup.
C3 - ICML Deep Learning Workshop
DA - 2015///
PY - 2015
DP - Google Scholar
UR - http://arxiv.org/abs/1506.06579
Y2 - 2015/12/03/T01:13:42Z
ER -
Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup.
Enhanced Flyby Science with Onboard Computer Vision: Tracking and Surface Feature Detection at Small Bodies.
Thomas J. Fuchs, David R. Thompson, Brian D. Bue, Julie Castillo-Rogez, Steve A. Chien, Dero Gharibian and Kiri L. Wagstaff.
Earth and Space Science, vol. 2, 10, p. 417-34, 2015
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{fuchs_enhanced_2015,
    title = {Enhanced {Flyby} {Science} with {Onboard} {Computer} {Vision}: {Tracking} and {Surface} {Feature} {Detection} at {Small} {Bodies}},
    volume = {2},
    issn = {2333-5084},
    shorttitle = {Enhanced flyby science with onboard computer vision},
    url = {http://onlinelibrary.wiley.com/doi/10.1002/2014EA000042/abstract},
    doi = {10.1002/2014EA000042},
    abstract = {Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.},
    language = {en},
    number = {10},
    urldate = {2015-11-22TZ},
    journal = {Earth and Space Science},
    author = {Fuchs, Thomas J. and Thompson, David R. and Bue, Brian D. and Castillo-Rogez, Julie and Chien, Steve A. and Gharibian, Dero and Wagstaff, Kiri L.},
    year = {2015},
    keywords = {0540 Image processing, 0555 Neural networks, fuzzy logic, machine learning, 6055 Surfaces, 6094 Instruments and techniques, 6205 Asteroids, asteroids, comets, computer vision, flyby, machine learning, small bodies},
    pages = {417--34}
}
Download Endnote/RIS citation
TY - JOUR
TI - Enhanced Flyby Science with Onboard Computer Vision: Tracking and Surface Feature Detection at Small Bodies
AU - Fuchs, Thomas J.
AU - Thompson, David R.
AU - Bue, Brian D.
AU - Castillo-Rogez, Julie
AU - Chien, Steve A.
AU - Gharibian, Dero
AU - Wagstaff, Kiri L.
T2 - Earth and Space Science
AB - Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.
DA - 2015///
PY - 2015
DO - 10.1002/2014EA000042
DP - Wiley Online Library
VL - 2
IS - 10
SP - 417
EP - 34
J2 - Earth and Space Science
LA - en
SN - 2333-5084
ST - Enhanced flyby science with onboard computer vision
UR - http://onlinelibrary.wiley.com/doi/10.1002/2014EA000042/abstract
Y2 - 2015/11/22/T21:25:18Z
KW - 0540 Image processing
KW - 0555 Neural networks, fuzzy logic, machine learning
KW - 6055 Surfaces
KW - 6094 Instruments and techniques
KW - 6205 Asteroids
KW - asteroids
KW - comets
KW - computer vision
KW - flyby
KW - machine learning
KW - small bodies
ER -
Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.
Boosting Convolutional Features for Robust Object Proposals.
Nikolaos Karianakis, Thomas J. Fuchs and Stefano Soatto.
2015
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{karianakis_boosting_2015,
    title = {Boosting {Convolutional} {Features} for {Robust} {Object} {Proposals}},
    url = {http://arxiv.org/abs/1503.06350},
    author = {Karianakis, Nikolaos and Fuchs, Thomas J. and Soatto, Stefano},
    year = {2015},
}
Download Endnote/RIS citation
TY - JOUR
TI - Boosting Convolutional Features for Robust Object Proposals
AU - Karianakis, Nikolaos
AU - Fuchs, Thomas J.
AU - Soatto, Stefano
DA - 2015///
PY - 2015
UR - http://arxiv.org/abs/1503.06350
ER -
Machine Learning Approaches to Analyze Histological Images of Tissues from Radical Prostatectomies.
Arkadiusz Gertych, Nathan Ing, Zhaoxuan Ma, Thomas J. Fuchs, Sadri Salman, Sambit Mohanty, Sanica Bhele, Adriana Velásquez-Vacca, Mahul B. Amin and Beatrice S. Knudsen.
Computerized Medical Imaging and Graphics, vol. 46, Part 2, p. 197-208, Information Technologies in Biomedicine, 2015
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@article{gertych_machine_2015,
    series = {Information {Technologies} in {Biomedicine}},
    title = {Machine {Learning} {Approaches} to {Analyze} {Histological} {Images} of {Tissues} from {Radical} {Prostatectomies}},
    volume = {46, Part 2},
    issn = {0895-6111},
    url = {http://www.sciencedirect.com/science/article/pii/S0895611115001184},
    doi = {10.1016/j.compmedimag.2015.08.002},
    abstract = {Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H\&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n = 19) and test (n = 191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN + PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J = 59.5 ± 14.6 and Rand Ri = 62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN = 35.2 ± 24.9, OBN = 49.6 ± 32, JPCa = 49.5 ± 18.5, OPCa = 72.7 ± 14.8 and Ri = 60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.},
    urldate = {2016-01-03TZ},
    journal = {Computerized Medical Imaging and Graphics},
    author = {Gertych, Arkadiusz and Ing, Nathan and Ma, Zhaoxuan and Fuchs, Thomas J. and Salman, Sadri and Mohanty, Sambit and Bhele, Sanica and Vel\'asquez-Vacca, Adriana and Amin, Mahul B. and Knudsen, Beatrice S.},
    year = {2015},
    keywords = {Image analysis, Prostate cancer, Tissue classification, Tissue quantification, machine learning},
    pages = {197--208}
}
Download Endnote/RIS citation
TY - JOUR
TI - Machine Learning Approaches to Analyze Histological Images of Tissues from Radical Prostatectomies
AU - Gertych, Arkadiusz
AU - Ing, Nathan
AU - Ma, Zhaoxuan
AU - Fuchs, Thomas J.
AU - Salman, Sadri
AU - Mohanty, Sambit
AU - Bhele, Sanica
AU - Velásquez-Vacca, Adriana
AU - Amin, Mahul B.
AU - Knudsen, Beatrice S.
T2 - Computerized Medical Imaging and Graphics
T3 - Information Technologies in Biomedicine
AB - Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n = 19) and test (n = 191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN + PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J = 59.5 ± 14.6 and Rand Ri = 62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN = 35.2 ± 24.9, OBN = 49.6 ± 32, JPCa = 49.5 ± 18.5, OPCa = 72.7 ± 14.8 and Ri = 60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.
DA - 2015///
PY - 2015
DO - 10.1016/j.compmedimag.2015.08.002
DP - ScienceDirect
VL - 46, Part 2
SP - 197
EP - 208
J2 - Computerized Medical Imaging and Graphics
SN - 0895-6111
UR - http://www.sciencedirect.com/science/article/pii/S0895611115001184
Y2 - 2016/01/03/T16:12:47Z
KW - Image analysis
KW - Prostate cancer
KW - Tissue classification
KW - Tissue quantification
KW - machine learning
ER -
Computerized evaluation of histological preparations of prostate tissues involves identification of tissue components such as stroma (ST), benign/normal epithelium (BN) and prostate cancer (PCa). Image classification approaches have been developed to identify and classify glandular regions in digital images of prostate tissues; however their success has been limited by difficulties in cellular segmentation and tissue heterogeneity. We hypothesized that utilizing image pixels to generate intensity histograms of hematoxylin (H) and eosin (E) stains deconvoluted from H&E images numerically captures the architectural difference between glands and stroma. In addition, we postulated that joint histograms of local binary patterns and local variance (LBPxVAR) can be used as sensitive textural features to differentiate benign/normal tissue from cancer. Here we utilized a machine learning approach comprising of a support vector machine (SVM) followed by a random forest (RF) classifier to digitally stratify prostate tissue into ST, BN and PCa areas. Two pathologists manually annotated 210 images of low- and high-grade tumors from slides that were selected from 20 radical prostatectomies and digitized at high-resolution. The 210 images were split into the training (n = 19) and test (n = 191) sets. Local intensity histograms of H and E were used to train a SVM classifier to separate ST from epithelium (BN + PCa). The performance of SVM prediction was evaluated by measuring the accuracy of delineating epithelial areas. The Jaccard J = 59.5 ± 14.6 and Rand Ri = 62.0 ± 7.5 indices reported a significantly better prediction when compared to a reference method (Chen et al., Clinical Proteomics 2013, 10:18) based on the averaged values from the test set. To distinguish BN from PCa we trained a RF classifier with LBPxVAR and local intensity histograms and obtained separate performance values for BN and PCa: JBN = 35.2 ± 24.9, OBN = 49.6 ± 32, JPCa = 49.5 ± 18.5, OPCa = 72.7 ± 14.8 and Ri = 60.6 ± 7.6 in the test set. Our pixel-based classification does not rely on the detection of lumens, which is prone to errors and has limitations in high-grade cancers and has the potential to aid in clinical studies in which the quantification of tumor content is necessary to prognosticate the course of the disease. The image data set with ground truth annotation is available for public use to stimulate further research in this area.
Risk-aware Planetary Rover Operation: Autonomous Terrain Classification and Path Planning.
Masahiro Ono, Thomas J. Fuchs, Amanda Steffy, Mark Maimone and Jeng Yen.
Proceedings of the 36th IEEE Aerospace Conference, p. 1–10, 2015
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{ono_risk-aware_2015,
    title = {Risk-aware {Planetary} {Rover} {Operation}: {Autonomous} {Terrain} {Classification} and {Path} {Planning}},
    url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7119022&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7119022},
    doi = {10.1109/AERO.2015.7119022},
    abstract = {Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed ground-based Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers. The risk-aware rover operation tool is built upon two technologies. The first technology is a machine learning-based terrain classification that is capable of identifying potential hazards, such as pointy rocks and soft terrains, from images. The second technology is a risk-aware path planner based on rapidly-exploring random graph (RRG) and the A* search algorithms, which is capable of avoiding hazards identified by the terrain classifier with explicitly considering wheel placement. We demonstrate the integrated capability of the proposed risk-aware rover operation tool by using the images taken by the Curiosity rover.},
    booktitle = {Proceedings of the 36th {IEEE} {Aerospace} {Conference}},
    author = {Ono, Masahiro and Fuchs, Thomas J. and Steffy, Amanda and Maimone, Mark and Yen, Jeng},
    year = {2015},
    pages = {1--10}
}
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TY - CONF
TI - Risk-aware Planetary Rover Operation: Autonomous Terrain Classification and Path Planning
AU - Ono, Masahiro
AU - Fuchs, Thomas J.
AU - Steffy, Amanda
AU - Maimone, Mark
AU - Yen, Jeng
AB - Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed ground-based Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers. The risk-aware rover operation tool is built upon two technologies. The first technology is a machine learning-based terrain classification that is capable of identifying potential hazards, such as pointy rocks and soft terrains, from images. The second technology is a risk-aware path planner based on rapidly-exploring random graph (RRG) and the A* search algorithms, which is capable of avoiding hazards identified by the terrain classifier with explicitly considering wheel placement. We demonstrate the integrated capability of the proposed risk-aware rover operation tool by using the images taken by the Curiosity rover.
C3 - Proceedings of the 36th IEEE Aerospace Conference
DA - 2015///
PY - 2015
DO - 10.1109/AERO.2015.7119022
SP - 1
EP - 10
UR - http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7119022&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7119022
ER -
Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed ground-based Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers. The risk-aware rover operation tool is built upon two technologies. The first technology is a machine learning-based terrain classification that is capable of identifying potential hazards, such as pointy rocks and soft terrains, from images. The second technology is a risk-aware path planner based on rapidly-exploring random graph (RRG) and the A* search algorithms, which is capable of avoiding hazards identified by the terrain classifier with explicitly considering wheel placement. We demonstrate the integrated capability of the proposed risk-aware rover operation tool by using the images taken by the Curiosity rover.
Robot-Centric Activity Prediction from First-Person Videos: What Will They Do to Me?
Michael S. Ryoo, Thomas J. Fuchs, Lu Xia, J. K. Aggarwal and Larry H. Matthies.
Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction, 2015
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@inproceedings{ryoo_robot-centric_2015,
    title = {Robot-{Centric} {Activity} {Prediction} from {First}-{Person} {Videos}: {What} {Will} {They} {Do} to {Me}?},
    url = {http://michaelryoo.com/papers/hri2015_ryoo.pdf},
    booktitle = {Proceedings of the 10th {ACM}/{IEEE} {International} {Conference} on {Human}-{Robot} {Interaction}},
    author = {Ryoo, Michael S. and Fuchs, Thomas J. and Xia, Lu and Aggarwal, J. K. and Matthies, Larry H.},
    year = {2015},
}
Download Endnote/RIS citation
TY - CONF
TI - Robot-Centric Activity Prediction from First-Person Videos: What Will They Do to Me?
AU - Ryoo, Michael S.
AU - Fuchs, Thomas J.
AU - Xia, Lu
AU - Aggarwal, J. K.
AU - Matthies, Larry H.
C3 - Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction
DA - 2015///
PY - 2015
UR - http://michaelryoo.com/papers/hri2015_ryoo.pdf
ER -
Crohn's Disease Segmentation from MRI Using Learned Image Priors.
D. Mahapatra, P. J. Schüffler, F. M. Vos and J. M. Buhmann.
Proceedings IEEE ISBI 2015, p. 625-628, 2015
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{mahapatra_crohns_2015,
    title = {Crohn's {Disease} {Segmentation} from {MRI} {Using} {Learned} {Image} {Priors}},
    url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951},
    abstract = {We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.},
    journal = {Proceedings IEEE ISBI 2015},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Vos, F. M. and Buhmann, J. M.},
    year = {2015},
    pages = {625--628}
}
Download Endnote/RIS citation
TY - JOUR
TI - Crohn's Disease Segmentation from MRI Using Learned Image Priors
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - Proceedings IEEE ISBI 2015
AB - We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
DA - 2015///
PY - 2015
SP - 625
EP - 628
UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951
ER -
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
Autonomous Real-time Detection of Plumes and Jets from Moons and Comets.
Kiri L. Wagstaff, David R. Thompson, Brian D. Bue and Thomas J. Fuchs.
ApJ, vol. 794, 1, p. 43, 2014
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{wagstaff_autonomous_2014,
    title = {Autonomous {Real}-time {Detection} of {Plumes} and {Jets} from {Moons} and {Comets}},
    volume = {794},
    issn = {0004-637X},
    url = {http://stacks.iop.org/0004-637X/794/i=1/a=43},
    doi = {10.1088/0004-637X/794/1/43},
    abstract = {Dynamic activity on the surface of distant moons, asteroids, and comets can manifest as jets or plumes. These phenomena provide information about the interior of the bodies and the forces (gravitation, radiation, thermal) they experience. Fast detection and follow-up study is imperative since the phenomena may be time-varying and because the observing window may be limited (e.g., during a flyby). We have developed an advanced method for real-time detection of plumes and jets using onboard analysis of the data as it is collected. In contrast to prior work, our technique is not restricted to plume detection from spherical bodies, making it relevant for irregularly shaped bodies such as comets. Further, our study analyzes raw data, the form in which it is available on board the spacecraft, rather than fully processed image products. In summary, we contribute a vital assessment of a technique that can be used on board tomorrow's deep space missions to detect, and respond quickly to, new occurrences of plumes and jets.},
    language = {en},
    number = {1},
    urldate = {2016-01-05TZ},
    journal = {The Astrophysical Journal},
    author = {Wagstaff, Kiri L. and Thompson, David R. and Bue, Brian D. and Fuchs, Thomas J.},
    year = {2014},
    pages = {43}
}
Download Endnote/RIS citation
TY - JOUR
TI - Autonomous Real-time Detection of Plumes and Jets from Moons and Comets
AU - Wagstaff, Kiri L.
AU - Thompson, David R.
AU - Bue, Brian D.
AU - Fuchs, Thomas J.
T2 - The Astrophysical Journal
AB - Dynamic activity on the surface of distant moons, asteroids, and comets can manifest as jets or plumes. These phenomena provide information about the interior of the bodies and the forces (gravitation, radiation, thermal) they experience. Fast detection and follow-up study is imperative since the phenomena may be time-varying and because the observing window may be limited (e.g., during a flyby). We have developed an advanced method for real-time detection of plumes and jets using onboard analysis of the data as it is collected. In contrast to prior work, our technique is not restricted to plume detection from spherical bodies, making it relevant for irregularly shaped bodies such as comets. Further, our study analyzes raw data, the form in which it is available on board the spacecraft, rather than fully processed image products. In summary, we contribute a vital assessment of a technique that can be used on board tomorrow's deep space missions to detect, and respond quickly to, new occurrences of plumes and jets.
DA - 2014///
PY - 2014
DO - 10.1088/0004-637X/794/1/43
DP - Institute of Physics
VL - 794
IS - 1
SP - 43
J2 - ApJ
LA - en
SN - 0004-637X
UR - http://stacks.iop.org/0004-637X/794/i=1/a=43
Y2 - 2016/01/05/T03:51:25Z
ER -
Dynamic activity on the surface of distant moons, asteroids, and comets can manifest as jets or plumes. These phenomena provide information about the interior of the bodies and the forces (gravitation, radiation, thermal) they experience. Fast detection and follow-up study is imperative since the phenomena may be time-varying and because the observing window may be limited (e.g., during a flyby). We have developed an advanced method for real-time detection of plumes and jets using onboard analysis of the data as it is collected. In contrast to prior work, our technique is not restricted to plume detection from spherical bodies, making it relevant for irregularly shaped bodies such as comets. Further, our study analyzes raw data, the form in which it is available on board the spacecraft, rather than fully processed image products. In summary, we contribute a vital assessment of a technique that can be used on board tomorrow's deep space missions to detect, and respond quickly to, new occurrences of plumes and jets.
Sparse Meta-Gaussian Information Bottleneck.
Melanie Rey, Thomas J. Fuchs and Volker Roth.
Proceedings of the 31st International Conference on Machine Learning, ICML, 2014
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@inproceedings{rey_sparse_2014,
    series = {{ICML}},
    title = {Sparse {Meta}-{Gaussian} {Information} {Bottleneck}},
    url = {http://jmlr.org/proceedings/papers/v32/rey14.pdf},
    booktitle = {Proceedings of the 31st {International} {Conference} on {Machine} {Learning}},
    author = {Rey, Melanie and Fuchs, Thomas J. and Roth, Volker},
    year = {2014},
}
Download Endnote/RIS citation
TY - CONF
TI - Sparse Meta-Gaussian Information Bottleneck
AU - Rey, Melanie
AU - Fuchs, Thomas J.
AU - Roth, Volker
T3 - ICML
C3 - Proceedings of the 31st International Conference on Machine Learning
DA - 2014///
PY - 2014
UR - http://jmlr.org/proceedings/papers/v32/rey14.pdf
ER -
KPNA2 Is Overexpressed in Human and Mouse Endometrial Cancers and Promotes Cellular Proliferation.
Kristian Ikenberg, Nadejda Valtcheva, Simone Brandt, Qing Zhong, Christine E. Wong, Aurelia Noske, Markus Rechsteiner, Jan H. Rueschoff, Rosemarie Caduff, Athanassios Dellas, Ellen Obermann, Daniel Fink, Thomas J. Fuchs, Wilhelm Krek, Holger Moch, Ian J. Frew and Peter J. Wild.
The Journal of Pathology, vol. 234, 2, p. 239–252, 2014
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@article{ikenberg_kpna2_2014,
    title = {{KPNA}2 {Is} {Overexpressed} in {Human} and {Mouse} {Endometrial} {Cancers} and {Promotes} {Cellular} {Proliferation}},
    volume = {234},
    issn = {1096-9896},
    url = {http://dx.doi.org/10.1002/path.4390},
    doi = {10.1002/path.4390},
    number = {2},
    journal = {The Journal of Pathology},
    author = {Ikenberg, Kristian and Valtcheva, Nadejda and Brandt, Simone and Zhong, Qing and Wong, Christine E. and Noske, Aurelia and Rechsteiner, Markus and Rueschoff, Jan H. and Caduff, Rosemarie and Dellas, Athanassios and Obermann, Ellen and Fink, Daniel and Fuchs, Thomas J. and Krek, Wilhelm and Moch, Holger and Frew, Ian J. and Wild, Peter J.},
    year = {2014},
    keywords = {EMT, KPNA2, Snail, biomarker, endometrial cancer, importin},
    pages = {239--252}
}
Download Endnote/RIS citation
TY - JOUR
TI - KPNA2 Is Overexpressed in Human and Mouse Endometrial Cancers and Promotes Cellular Proliferation
AU - Ikenberg, Kristian
AU - Valtcheva, Nadejda
AU - Brandt, Simone
AU - Zhong, Qing
AU - Wong, Christine E.
AU - Noske, Aurelia
AU - Rechsteiner, Markus
AU - Rueschoff, Jan H.
AU - Caduff, Rosemarie
AU - Dellas, Athanassios
AU - Obermann, Ellen
AU - Fink, Daniel
AU - Fuchs, Thomas J.
AU - Krek, Wilhelm
AU - Moch, Holger
AU - Frew, Ian J.
AU - Wild, Peter J.
T2 - The Journal of Pathology
DA - 2014///
PY - 2014
DO - 10.1002/path.4390
VL - 234
IS - 2
SP - 239
EP - 252
SN - 1096-9896
UR - http://dx.doi.org/10.1002/path.4390
KW - EMT
KW - KPNA2
KW - Snail
KW - biomarker
KW - endometrial cancer
KW - importin
ER -
Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry.
C. Giesen, H. A. Wang, D. Schapiro, N. Zivanovic, A. Jacobs, B. Hattendorf, P. J. Schüffler, D. Grolimund, J. M. Buhmann, S. Brandt, Z. Varga, P. J. Wild, D. Gunther and B. Bodenmiller.
Nature methods, vol. 11, p. 417-22, 2014
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@article{giesen_highly_2014,
    title = {Highly {Multiplexed} {Imaging} of {Tumor} {Tissues} with {Subcellular} {Resolution} by {Mass} {Cytometry}},
    volume = {11},
    issn = {1548-7105 (Electronic) 1548-7091 (Linking)},
    url = {http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html},
    doi = {10.1038/nmeth.2869},
    abstract = {Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.},
    journal = {Nat Methods},
    author = {Giesen, C. and Wang, H. A. and Schapiro, D. and Zivanovic, N. and Jacobs, A. and Hattendorf, B. and Sch\"uffler, P. J. and Grolimund, D. and Buhmann, J. M. and Brandt, S. and Varga, Z. and Wild, P. J. and Gunther, D. and Bodenmiller, B.},
    year = {2014},
    pages = {417--22}
}
Download Endnote/RIS citation
TY - JOUR
TI - Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry
AU - Giesen, C.
AU - Wang, H. A.
AU - Schapiro, D.
AU - Zivanovic, N.
AU - Jacobs, A.
AU - Hattendorf, B.
AU - Schüffler, P. J.
AU - Grolimund, D.
AU - Buhmann, J. M.
AU - Brandt, S.
AU - Varga, Z.
AU - Wild, P. J.
AU - Gunther, D.
AU - Bodenmiller, B.
T2 - Nat Methods
AB - Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
DA - 2014///
PY - 2014
DO - 10.1038/nmeth.2869
VL - 11
SP - 417
EP - 22
J2 - Nature methods
SN - 1548-7105 (Electronic) 1548-7091 (Linking)
UR - http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html
ER -
Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys.
S.G. Djorgovski, A. Mahabal, C. Donalek, M. Graham, A. Drake, M. Turmon and T.J. Fuchs.
2014 IEEE 10th International Conference on e-Science (e-Science), 2014 IEEE 10th International Conference on e-Science (e-Science), vol. 1, p. 204-211, 2014
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{djorgovski_automated_2014,
    title = {Automated {Real}-{Time} {Classification} and {Decision} {Making} in {Massive} {Data} {Streams} from {Synoptic} {Sky} {Surveys}},
    volume = {1},
    url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6972266&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6972266},
    doi = {10.1109/eScience.2014.7},
    abstract = {The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.},
    booktitle = {2014 {IEEE} 10th {International} {Conference} on e-{Science} (e-{Science})},
    author = {Djorgovski, S.G. and Mahabal, A. and Donalek, C. and Graham, M. and Drake, A. and Turmon, M. and Fuchs, T.J.},
    year = {2014},
    keywords = {Astronomy, Automated decision making, Bayesian methods, CRTS, Catalina Real-time Transient Survey, Cathode ray tubes, Data analysis, Extraterrestrial measurements, Massive data streams, Pollution measurement, Real-time systems, Sky surveys, Time measurement, Transient analysis, astronomical surveys, astronomy applications, astronomy computing, automated real-time classification, automated real-time decision making, black hole formation, classification, cosmic explosions, decision making, digital synoptic sky surveys, gamma ray bursts, jets, learning (artificial intelligence), machine learning, machine learning tools, pattern classification, potentially hazardous asteroids, relativistic phenomena, scientific data collection, supernovae, technological data collection},
    pages = {204--211}
}
Download Endnote/RIS citation
TY - CONF
TI - Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys
AU - Djorgovski, S.G.
AU - Mahabal, A.
AU - Donalek, C.
AU - Graham, M.
AU - Drake, A.
AU - Turmon, M.
AU - Fuchs, T.J.
T2 - 2014 IEEE 10th International Conference on e-Science (e-Science)
AB - The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
C3 - 2014 IEEE 10th International Conference on e-Science (e-Science)
DA - 2014///
PY - 2014
DO - 10.1109/eScience.2014.7
DP - IEEE Xplore
VL - 1
SP - 204
EP - 211
UR - http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6972266&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6972266
KW - Astronomy
KW - Automated decision making
KW - Bayesian methods
KW - CRTS
KW - Catalina Real-time Transient Survey
KW - Cathode ray tubes
KW - Data analysis
KW - Extraterrestrial measurements
KW - Massive data streams
KW - Pollution measurement
KW - Real-time systems
KW - Sky surveys
KW - Time measurement
KW - Transient analysis
KW - astronomical surveys
KW - astronomy applications
KW - astronomy computing
KW - automated real-time classification
KW - automated real-time decision making
KW - black hole formation
KW - classification
KW - cosmic explosions
KW - decision making
KW - digital synoptic sky surveys
KW - gamma ray bursts
KW - jets
KW - learning (artificial intelligence)
KW - machine learning
KW - machine learning tools
KW - pattern classification
KW - potentially hazardous asteroids
KW - relativistic phenomena
KW - scientific data collection
KW - supernovae
KW - technological data collection
ER -
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.
Early Recognition of Human Activities from First-Person Videos Using Onset Representations.
M. S. Ryoo, Thomas J. Fuchs, Lu Xia, J. K. Aggarwal and Larry Matthies.
arXiv:1406.5309 [cs], 2014
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{ryoo_early_2014,
    title = {Early {Recognition} of {Human} {Activities} from {First}-{Person} {Videos} {Using} {Onset} {Representations}},
    url = {http://arxiv.org/abs/1406.5309},
    abstract = {In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos.},
    urldate = {2015-12-23TZ},
    journal = {arXiv:1406.5309 [cs]},
    author = {Ryoo, M. S. and Fuchs, Thomas J. and Xia, Lu and Aggarwal, J. K. and Matthies, Larry},
    year = {2014},
    keywords = {Computer Science - Computer Vision and Pattern Recognition}
}
Download Endnote/RIS citation
TY - JOUR
TI - Early Recognition of Human Activities from First-Person Videos Using Onset Representations
AU - Ryoo, M. S.
AU - Fuchs, Thomas J.
AU - Xia, Lu
AU - Aggarwal, J. K.
AU - Matthies, Larry
T2 - arXiv:1406.5309 [cs]
AB - In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos.
DA - 2014///
PY - 2014
DP - arXiv.org
UR - http://arxiv.org/abs/1406.5309
Y2 - 2015/12/23/T16:01:25Z
KW - Computer Science - Computer Vision and Pattern Recognition
ER -
In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos.
Computer Aided Crohn's Disease Severity Assessment in MRI.
Peter J. Schüffler, Dwarikanath Mahapatra, Franciscus M. Vos and Joachim M. Buhmann.
VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook, 2014
Best Poster Award
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@misc{schuffler_computer_2014,
    address = {London},
    type = {Poster},
    title = {Computer {Aided} {Crohn}'s {Disease} {Severity} {Assessment} in {MRI}},
    url = {https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Vos, Franciscus M. and Buhmann, Joachim M.},
    year = {2014},
}
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TY - SLIDE
TI - Computer Aided Crohn's Disease Severity Assessment in MRI
T2 - VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook
A2 - Schüffler, Peter J.
A2 - Mahapatra, Dwarikanath
A2 - Vos, Franciscus M.
A2 - Buhmann, Joachim M.
CY - London
DA - 2014///
PY - 2014
M3 - Poster
UR - https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI
ER -
Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging.
P. J. Schüffler, D. Mahapatra, R. E. Naziroglu, Z. Li, C. A. J. Puylaert, R. Andriantsimiavona, F. M. Vos, D. A. Pendsé, C. Yung Nio, J. Stoker, S. A. Taylor and J. M. Buhmann.
6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications, 2014
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schuffler_semi-automatic_2014,
    title = {Semi-{Automatic} {Crohn}'s {Disease} {Severity} {Estimation} on {MR} {Imaging}},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12},
    abstract = {Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).},
    author = {Sch\"uffler, P. J. and Mahapatra, D. and Naziroglu, R. E. and Li, Z. and Puylaert, C. A. J. and Andriantsimiavona, R. and Vos, F. M. and Pends\'e, D. A. and Nio, C. Yung and Stoker, J. and Taylor, S. A. and Buhmann, J. M.},
    year = {2014},
}
Download Endnote/RIS citation
TY - CONF
TI - Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging
AU - Schüffler, P. J.
AU - Mahapatra, D.
AU - Naziroglu, R. E.
AU - Li, Z.
AU - Puylaert, C. A. J.
AU - Andriantsimiavona, R.
AU - Vos, F. M.
AU - Pendsé, D. A.
AU - Nio, C. Yung
AU - Stoker, J.
AU - Taylor, S. A.
AU - Buhmann, J. M.
T2 - 6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications
AB - Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
DA - 2014///
PY - 2014
UR - http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12
ER -
Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
Autonomous Onboard Surface Feature Detection for Flyby Missions.
Thomas J. Fuchs, Brian D. Bue, Julie Castillo-Rogez, Steve A. Chien, Kiri Wagstaff and David R. Thompson.
Proceedings of the 12th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), 2014
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@inproceedings{fuchs_autonomous_2014,
    title = {Autonomous {Onboard} {Surface} {Feature} {Detection} for {Flyby} {Missions}},
    booktitle = {Proceedings of the 12th {International} {Symposium} on {Artificial} {Intelligence}, {Robotics} and {Automation} in {Space} (i-{SAIRAS})},
    author = {Fuchs, Thomas J. and Bue, Brian D. and Castillo-Rogez, Julie and Chien, Steve A. and Wagstaff, Kiri and Thompson, David R.},
    year = {2014},
}
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TY - CONF
TI - Autonomous Onboard Surface Feature Detection for Flyby Missions
AU - Fuchs, Thomas J.
AU - Bue, Brian D.
AU - Castillo-Rogez, Julie
AU - Chien, Steve A.
AU - Wagstaff, Kiri
AU - Thompson, David R.
C3 - Proceedings of the 12th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS)
DA - 2014///
PY - 2014
ER -
TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation.
Peter J. Schüffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
Journal of Pathology Informatics, vol. 4, 2, p. 2, 2013
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@article{schuffler_tmarker:_2013,
    title = {{TMARKER}: {A} {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Staining} {Estimation}},
    volume = {4},
    url = {http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=2;epage=2;aulast=Sch%FCffler;t=6},
    doi = {10.4103/2153-3539.109804},
    abstract = {Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.},
    number = {2},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2013},
    pages = {2}
}
Download Endnote/RIS citation
TY - JOUR
TI - TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - Journal of Pathology Informatics
AB - Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
DA - 2013///
PY - 2013
DO - 10.4103/2153-3539.109804
VL - 4
IS - 2
SP - 2
UR - http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=2;epage=2;aulast=Sch%FCffler;t=6
ER -
Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
Smart, Texture-Sensitive Instrument Classification for in Situ Rock and Layer Analysis.
K. L. Wagstaff, D. R. Thompson, W. Abbey, A. Allwood, D. Bekker, N. A. Cabrol, Thomas J. Fuchs and K. Ortega.
Geophysical Research Letters, vol. 40, 16, p. 4188–4193, 2013
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@article{wagstaff_smart_2013,
    title = {Smart, {Texture}-{Sensitive} {Instrument} {Classification} for in {Situ} {Rock} and {Layer} {Analysis}},
    volume = {40},
    issn = {1944-8007},
    url = {http://dx.doi.org/10.1002/grl.50817},
    doi = {10.1002/grl.50817},
    number = {16},
    journal = {Geophysical Research Letters},
    author = {Wagstaff, K. L. and Thompson, D. R. and Abbey, W. and Allwood, A. and Bekker, D. and Cabrol, N. A. and Fuchs, Thomas J. and Ortega, K.},
    year = {2013},
    pages = {4188--4193}
}
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TY - JOUR
TI - Smart, Texture-Sensitive Instrument Classification for in Situ Rock and Layer Analysis
AU - Wagstaff, K. L.
AU - Thompson, D. R.
AU - Abbey, W.
AU - Allwood, A.
AU - Bekker, D.
AU - Cabrol, N. A.
AU - Fuchs, Thomas J.
AU - Ortega, K.
T2 - Geophysical Research Letters
DA - 2013///
PY - 2013
DO - 10.1002/grl.50817
VL - 40
IS - 16
SP - 4188
EP - 4193
SN - 1944-8007
UR - http://dx.doi.org/10.1002/grl.50817
ER -
TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations.
Peter J. Schueffler, Niels Rupp, Cheng S. Ong, Joachim M. Buhmann, Thomas J. Fuchs and Peter J. Wild.
German Society of Pathology 97th Annual Meeting, 2013
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schueffler_tmarker:_2013,
    title = {{TMARKER}: {A} {Robust} and {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Immunohistochemical} {Staining} {Estimations}},
    url = {http://link.springer.com/article/10.1007%2Fs00292-013-1765-2},
    abstract = {Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
    Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
    Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
    Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.},
    booktitle = {German {Society} of {Pathology} 97th {Annual} {Meeting}},
    author = {Schueffler, Peter J. and Rupp, Niels and Ong, Cheng S. and Buhmann, Joachim M. and Fuchs, Thomas J. and Wild, Peter J.},
    year = {2013},
}
Download Endnote/RIS citation
TY - CONF
TI - TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations
AU - Schueffler, Peter J.
AU - Rupp, Niels
AU - Ong, Cheng S.
AU - Buhmann, Joachim M.
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AB - Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
C3 - German Society of Pathology 97th Annual Meeting
DA - 2013///
PY - 2013
UR - http://link.springer.com/article/10.1007%2Fs00292-013-1765-2
ER -
Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming. Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision. Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas. Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma.
Peter J. Schueffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann.
In: Similarity-Based Pattern Analysis and Recognition, p. 219–246, Advances in Computer Vision and Pattern Recognition, Springer, ISBN 978-1-4471-5627-7, 2013
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@incollection{schueffler_automated_2013,
    address = {London},
    series = {Advances in {Computer} {Vision} and {Pattern} {Recognition}},
    title = {Automated {Analysis} of {Tissue} {Micro}-{Array} {Images} on the {Example} of {Renal} {Cell} {Carcinoma}},
    isbn = {978-1-4471-5627-7},
    url = {https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7},
    abstract = {Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.},
    booktitle = {Similarity-{Based} {Pattern} {Analysis} and {Recognition}},
    publisher = {Springer},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2013},
    pages = {219--246}
}
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TY - CHAP
TI - Automated Analysis of Tissue Micro-Array Images on the Example of Renal Cell Carcinoma
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng Soon
AU - Roth, Volker
AU - Buhmann, Joachim M.
T2 - Similarity-Based Pattern Analysis and Recognition
T3 - Advances in Computer Vision and Pattern Recognition
AB - Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.
CY - London
DA - 2013///
PY - 2013
SP - 219
EP - 246
PB - Springer
SN - 978-1-4471-5627-7
UR - https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7
ER -
Automated tissue micro-array analysis forms a challenging problem in computational pathology. The detection of cell nuclei, the classification into malignant and benign as well as the evaluation of their protein expression pattern by immunohistochemical staining are crucial routine steps for human cancer research and oncology. Computational assistance in this field can extremely accelerate the high throughput of the upcoming patient data as well as facilitate the reproducibility and objectivity of qualitative and quantitative measures. In this chapter, we describe an automated pipeline for staining estimation of tissue micro-array images, which comprises nucleus detection, nucleus segmentation, nucleus classification and staining estimation among cancerous nuclei. This pipeline is a practical example for the importance of non-metric effects in this kind of image analysis, e.g., the use of shape information and non-Euclidean kernels improve the nucleus classification performance significantly. The pipeline is explained and validated on a renal clear cell carcinoma dataset with MIB-1 stained tissue micro-array images and survival data of 133 patients. Further, the pipeline is implemented for medical use and research purpose in the free program TMARKER.
A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images.
Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos, Jesica Makanyanga, Doug A. Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor and Joachim M. Buhmann.
In: Hiroyuki Yoshida, Simon Warfield and Michael Vannier (eds.) Abdominal Imaging. Computation and Clinical Applications, vol. 8198, p. 1-10, Lecture Notes in Computer Science, Springer Berlin Heidelberg, ISBN 978-3-642-41082-6, 2013
Outstanding Paper Award
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@incollection{schuffler_model_2013,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {A {Model} {Development} {Pipeline} for {Crohn}'s {Disease} {Severity} {Assessment} from {Magnetic} {Resonance} {Images}},
    volume = {8198},
    isbn = {978-3-642-41082-6},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1},
    abstract = {Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.},
    booktitle = {Abdominal {Imaging}. {Computation} and {Clinical} {Applications}},
    publisher = {Springer Berlin Heidelberg},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Tielbeek, Jeroen A. W. and Vos, Franciscus M. and Makanyanga, Jesica and Pends\'e, Doug A. and Nio, C. Yung and Stoker, Jaap and Taylor, Stuart A. and Buhmann, Joachim M.},
    editor = {Yoshida, Hiroyuki and Warfield, Simon and Vannier, Michael},
    year = {2013},
    keywords = {AIS, CDEIS, Crohn’s Disease, MaRIA, abdominal MRI},
    pages = {1--10}
}
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TY - CHAP
TI - A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images
AU - Schüffler, Peter J.
AU - Mahapatra, Dwarikanath
AU - Tielbeek, Jeroen A. W.
AU - Vos, Franciscus M.
AU - Makanyanga, Jesica
AU - Pendsé, Doug A.
AU - Nio, C. Yung
AU - Stoker, Jaap
AU - Taylor, Stuart A.
AU - Buhmann, Joachim M.
T2 - Abdominal Imaging. Computation and Clinical Applications
A2 - Yoshida, Hiroyuki
A2 - Warfield, Simon
A2 - Vannier, Michael
T3 - Lecture Notes in Computer Science
AB - Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
DA - 2013///
PY - 2013
VL - 8198
SP - 1
EP - 10
PB - Springer Berlin Heidelberg
SN - 978-3-642-41082-6
UR - http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1
KW - AIS
KW - CDEIS
KW - Crohn’s Disease
KW - MaRIA
KW - abdominal MRI
ER -
Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets.
Ciro Donalek, Arun Kumar A., S. G. Djorgovski, Ashish A. Mahabal, Matthew J. Graham, Thomas J. Fuchs, Michael J. Turmon, N. Sajeeth Philip, Michael Ting-Chang Yang and Giuseppe Longo.
IEEE International Conference on Big Data, 2013
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@article{donalek_feature_2013,
    title = {Feature {Selection} {Strategies} for {Classifying} {High} {Dimensional} {Astronomical} {Data} {Sets}},
    url = {http://arxiv.org/abs/1310.1976},
    abstract = {The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.},
    urldate = {2016-01-05TZ},
    journal = {IEEE International Conference on Big Data},
    author = {Donalek, Ciro and A., Arun Kumar and Djorgovski, S. G. and Mahabal, Ashish A. and Graham, Matthew J. and Fuchs, Thomas J. and Turmon, Michael J. and Philip, N. Sajeeth and Yang, Michael Ting-Chang and Longo, Giuseppe},
    year = {2013},
    keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computer Vision and Pattern Recognition}
}
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TY - JOUR
TI - Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets
AU - Donalek, Ciro
AU - A., Arun Kumar
AU - Djorgovski, S. G.
AU - Mahabal, Ashish A.
AU - Graham, Matthew J.
AU - Fuchs, Thomas J.
AU - Turmon, Michael J.
AU - Philip, N. Sajeeth
AU - Yang, Michael Ting-Chang
AU - Longo, Giuseppe
T2 - IEEE International Conference on Big Data
AB - The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.
DA - 2013///
PY - 2013
DP - arXiv.org
UR - http://arxiv.org/abs/1310.1976
Y2 - 2016/01/05/T04:03:47Z
KW - Astrophysics - Instrumentation and Methods for Astrophysics
KW - Computer Science - Computer Vision and Pattern Recognition
ER -
The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems.
Quickly Boosting Decision Trees – Pruning Underachieving Features using a Provable Bound.
Ron Appel, Piotr Dollar, Thomas J. Fuchs and Pietro Perona.
Proceedings of the 30th International Conference on Machine Learning (ICML), vol. 28, p. 594-602, 2013
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@inproceedings{appel_quickly_2013,
    title = {Quickly {Boosting} {Decision} {Trees} – {Pruning} {Underachieving} {Features} using a {Provable} {Bound}},
    volume = {28},
    url = {http://jmlr.org/proceedings/papers/v28/appel13.html},
    booktitle = {Proceedings of the 30th {International} {Conference} on {Machine} {Learning} ({ICML})},
    author = {Appel, Ron and Dollar, Piotr and Fuchs, Thomas J. and Perona, Pietro},
    year = {2013},
    pages = {594--602}
}
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TY - CONF
TI - Quickly Boosting Decision Trees – Pruning Underachieving Features using a Provable Bound
AU - Appel, Ron
AU - Dollar, Piotr
AU - Fuchs, Thomas J.
AU - Perona, Pietro
C3 - Proceedings of the 30th International Conference on Machine Learning (ICML)
DA - 2013///
PY - 2013
VL - 28
SP - 594
EP - 602
UR - http://jmlr.org/proceedings/papers/v28/appel13.html
ER -
Recognizing Humans in Motion: Trajectory-based Areal Video Analysis.
Yumi Iwashita, Michael Ryoo, Thomas J. Fuchs and Curtis Padgett.
24th British Machine Vision Conference (BMVC), 2013
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@inproceedings{iwashita_recognizing_2013,
    title = {Recognizing {Humans} in {Motion}: {Trajectory}-based {Areal} {Video} {Analysis}},
    booktitle = {24th {British} {Machine} {Vision} {Conference} ({BMVC})},
    author = {Iwashita, Yumi and Ryoo, Michael and Fuchs, Thomas J. and Padgett, Curtis},
    year = {2013},
}
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TY - CONF
TI - Recognizing Humans in Motion: Trajectory-based Areal Video Analysis
AU - Iwashita, Yumi
AU - Ryoo, Michael
AU - Fuchs, Thomas J.
AU - Padgett, Curtis
C3 - 24th British Machine Vision Conference (BMVC)
DA - 2013///
PY - 2013
ER -
Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI.
D. Mahapatra, P. J. Schüffler, J. A. W. Tielbeek, J. C. Makanyanga, J. Stoker, S. A. Taylor, F. M. Vos and J. M. Buhmann.
IEEE Transactions on Medical Imaging, vol. 32, p. 2332-2347, 2013
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@article{mahapatra_automatic_2013,
    title = {Automatic {Detection} and {Segmentation} of {Crohn}'s {Disease} {Tissues} from {Abdominal} {MRI}},
    volume = {32},
    issn = {0278-0062},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949},
    doi = {10.1109/TMI.2013.2282124},
    abstract = {We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.},
    journal = {IEEE Transactions on Medical Imaging},
    author = {Mahapatra, D. and Sch\"uffler, P. J. and Tielbeek, J. A. W. and Makanyanga, J. C. and Stoker, J. and Taylor, S. A. and Vos, F. M. and Buhmann, J. M.},
    year = {2013},
    keywords = {Anisotropic magnetoresistance, Context, Crohn\&\#x2019, Diseases, Entropy, Image segmentation, Radio frequency, content, graph cut, image features, probability maps, random forests, s disease, segmentation, semantic information, shape, supervoxels},
    pages = {2332--2347}
}
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TY - JOUR
TI - Automatic Detection and Segmentation of Crohn's Disease Tissues from Abdominal MRI
AU - Mahapatra, D.
AU - Schüffler, P. J.
AU - Tielbeek, J. A. W.
AU - Makanyanga, J. C.
AU - Stoker, J.
AU - Taylor, S. A.
AU - Vos, F. M.
AU - Buhmann, J. M.
T2 - IEEE Transactions on Medical Imaging
AB - We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
DA - 2013///
PY - 2013
DO - 10.1109/TMI.2013.2282124
VL - 32
SP - 2332
EP - 2347
SN - 0278-0062
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6600949
KW - Anisotropic magnetoresistance
KW - Context
KW - Crohn’
KW - Diseases
KW - Entropy
KW - Image segmentation
KW - Radio frequency
KW - content
KW - graph cut
KW - image features
KW - probability maps
KW - random forests
KW - s disease
KW - segmentation
KW - semantic information
KW - shape
KW - supervoxels
ER -
We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers. The supervoxels identified as containing diseased tissues define the volume of interest (VOI). All voxels within the VOI are further investigated to segment the diseased region. Probability maps are generated for each voxel using a second set of RF classifiers which give the probabilities of each voxel being diseased, normal or background. The negative log-likelihood of these maps are used as penalty costs in a graph cut segmentation framework. Low level features like intensity statistics, texture anisotropy and curvature asymmetry, and high level context features are used at different stages. Smoothness constraints are imposed based on semantic information (importance of each feature to the classification task) derived from the second set of learned RF classifiers. Experimental results show that our method achieves high segmentation accuracy with Dice metric values of 0.90 ± 0.04 and Hausdorff distance of 7.3 ± 0.8 mm. Semantic information and context features are an integral part of our method and are robust to different levels of added noise.
TextureCam: A Smart Camera for Microscale, Mesoscale, and Deep Space.
William Abbey, Abigail Allwood, Dmitriy Bekker, Benjamin Bornstein, Nathalie A. Cabrol, Rebecca Castano, Steve A. Chien, Joshua Doubleday, Tara Estlin, Greydon Foil, Thomas J. Fuchs, Daniel Howarth, Kevin Ortega, David R. Thompson and Kiri L. Wagstaff.
44th Lunar and Planetary Science Conference, p. 2209, 2013
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@inproceedings{abbey_texturecam:_2013,
    title = {{TextureCam}: {A} {Smart} {Camera} for {Microscale}, {Mesoscale}, and {Deep} {Space}},
    booktitle = {44th {Lunar} and {Planetary} {Science} {Conference}},
    author = {Abbey, William and Allwood, Abigail and Bekker, Dmitriy and Bornstein, Benjamin and Cabrol, Nathalie A. and Castano, Rebecca and Chien, Steve A. and Doubleday, Joshua and Estlin, Tara and Foil, Greydon and Fuchs, Thomas J. and Howarth, Daniel and Ortega, Kevin and Thompson, David R. and Wagstaff, Kiri L.},
    year = {2013},
    pages = {2209}
}
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TY - CONF
TI - TextureCam: A Smart Camera for Microscale, Mesoscale, and Deep Space
AU - Abbey, William
AU - Allwood, Abigail
AU - Bekker, Dmitriy
AU - Bornstein, Benjamin
AU - Cabrol, Nathalie A.
AU - Castano, Rebecca
AU - Chien, Steve A.
AU - Doubleday, Joshua
AU - Estlin, Tara
AU - Foil, Greydon
AU - Fuchs, Thomas J.
AU - Howarth, Daniel
AU - Ortega, Kevin
AU - Thompson, David R.
AU - Wagstaff, Kiri L.
C3 - 44th Lunar and Planetary Science Conference
DA - 2013///
PY - 2013
SP - 2209
ER -
Structure Preserving Embedding of Dissimilarity Data.
Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran and Joachim M. Buhmann.
In: Similarity-Based Pattern Analysis and Recognition, p. 157–178, Advances in Computer Vision and Pattern Recognition, Springer, ISBN 978-1-4471-5627-7, 2013
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@incollection{roth_structure_2013,
    series = {Advances in {Computer} {Vision} and {Pattern} {Recognition}},
    title = {Structure {Preserving} {Embedding} of {Dissimilarity} {Data}},
    isbn = {978-1-4471-5627-7},
    url = {https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7},
    booktitle = {Similarity-{Based} {Pattern} {Analysis} and {Recognition}},
    publisher = {Springer},
    author = {Roth, Volker and Fuchs, Thomas J. and Vogt, Julia E. and Prabhakaran, Sandhya and Buhmann, Joachim M.},
    year = {2013},
    pages = {157--178}
}
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TY - CHAP
TI - Structure Preserving Embedding of Dissimilarity Data
AU - Roth, Volker
AU - Fuchs, Thomas J.
AU - Vogt, Julia E.
AU - Prabhakaran, Sandhya
AU - Buhmann, Joachim M.
T2 - Similarity-Based Pattern Analysis and Recognition
T3 - Advances in Computer Vision and Pattern Recognition
DA - 2013///
PY - 2013
SP - 157
EP - 178
PB - Springer
SN - 978-1-4471-5627-7
UR - https://www.springer.com/computer/image+processing/book/978-1-4471-5627-7
ER -
p53 Suppresses Type II Endometrial Carcinomas in Mice and Governs Endometrial Tumour Aggressiveness in Humans.
Peter J. Wild, Kristian Ikenberg, Thomas J. Fuchs, Markus Rechsteiner, Strahil Georgiev, Niklaus Fankhauser, Aurelia Noske, Matthias Roessle, Rosmarie Caduff, Athanassios Dellas, Daniel Fink, Holger Moch, Wilhelm Krek and Ian J. Frew.
EMBO Molecular Medicine, vol. 4, 8, p. 808-824, 2012
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@article{wild_p53_2012,
    title = {p53 {Suppresses} {Type} {II} {Endometrial} {Carcinomas} in {Mice} and {Governs} {Endometrial} {Tumour} {Aggressiveness} in {Humans}},
    volume = {4},
    issn = {17574676},
    shorttitle = {p53 suppresses type {II} endometrial carcinomas in mice and governs endometrial tumour aggressiveness in humans},
    url = {http://embomolmed.embopress.org/cgi/doi/10.1002/emmm.201101063},
    doi = {10.1002/emmm.201101063},
    abstract = {Type II endometrial carcinomas are a highly aggressive group of tumour subtypes that are frequently associated with inactivation of the TP53 tumour suppressor gene. We show that mice with endometrium-specific deletion of Trp53 initially exhibited histological changes that are identical to known precursor lesions of type II endometrial carcinomas in humans and later developed carcinomas representing all type II subtypes. The mTORC1 signalling pathway was frequently activated in these precursor lesions and tumours, suggesting a genetic cooperation between this pathway and Trp53 deficiency in tumour initiation. Consistent with this idea, analyses of 521 human endometrial carcinomas identified frequent mTORC1 pathway activation in type I as well as type II endometrial carcinoma subtypes. mTORC1 pathway activation and p53 expression or mutation status each independently predicted poor patient survival. We suggest that molecular alterations in p53 and the mTORC1 pathway play different roles in the initiation of the different endometrial cancer subtypes, but that combined p53 inactivation and mTORC1 pathway activation are unifying pathogenic features among histologically diverse subtypes of late stage aggressive endometrial tumours.},
    language = {en},
    number = {8},
    urldate = {2016-04-12TZ},
    journal = {EMBO Molecular Medicine},
    author = {Wild, Peter J. and Ikenberg, Kristian and Fuchs, Thomas J. and Rechsteiner, Markus and Georgiev, Strahil and Fankhauser, Niklaus and Noske, Aurelia and Roessle, Matthias and Caduff, Rosmarie and Dellas, Athanassios and Fink, Daniel and Moch, Holger and Krek, Wilhelm and Frew, Ian J.},
    year = {2012},
    pages = {808--824}
}
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TY - JOUR
TI - p53 Suppresses Type II Endometrial Carcinomas in Mice and Governs Endometrial Tumour Aggressiveness in Humans
AU - Wild, Peter J.
AU - Ikenberg, Kristian
AU - Fuchs, Thomas J.
AU - Rechsteiner, Markus
AU - Georgiev, Strahil
AU - Fankhauser, Niklaus
AU - Noske, Aurelia
AU - Roessle, Matthias
AU - Caduff, Rosmarie
AU - Dellas, Athanassios
AU - Fink, Daniel
AU - Moch, Holger
AU - Krek, Wilhelm
AU - Frew, Ian J.
T2 - EMBO Molecular Medicine
AB - Type II endometrial carcinomas are a highly aggressive group of tumour subtypes that are frequently associated with inactivation of the TP53 tumour suppressor gene. We show that mice with endometrium-specific deletion of Trp53 initially exhibited histological changes that are identical to known precursor lesions of type II endometrial carcinomas in humans and later developed carcinomas representing all type II subtypes. The mTORC1 signalling pathway was frequently activated in these precursor lesions and tumours, suggesting a genetic cooperation between this pathway and Trp53 deficiency in tumour initiation. Consistent with this idea, analyses of 521 human endometrial carcinomas identified frequent mTORC1 pathway activation in type I as well as type II endometrial carcinoma subtypes. mTORC1 pathway activation and p53 expression or mutation status each independently predicted poor patient survival. We suggest that molecular alterations in p53 and the mTORC1 pathway play different roles in the initiation of the different endometrial cancer subtypes, but that combined p53 inactivation and mTORC1 pathway activation are unifying pathogenic features among histologically diverse subtypes of late stage aggressive endometrial tumours.
DA - 2012///
PY - 2012
DO - 10.1002/emmm.201101063
DP - CrossRef
VL - 4
IS - 8
SP - 808
EP - 824
LA - en
SN - 17574676
ST - p53 suppresses type II endometrial carcinomas in mice and governs endometrial tumour aggressiveness in humans
UR - http://embomolmed.embopress.org/cgi/doi/10.1002/emmm.201101063
Y2 - 2016/04/12/T13:58:18Z
ER -
Type II endometrial carcinomas are a highly aggressive group of tumour subtypes that are frequently associated with inactivation of the TP53 tumour suppressor gene. We show that mice with endometrium-specific deletion of Trp53 initially exhibited histological changes that are identical to known precursor lesions of type II endometrial carcinomas in humans and later developed carcinomas representing all type II subtypes. The mTORC1 signalling pathway was frequently activated in these precursor lesions and tumours, suggesting a genetic cooperation between this pathway and Trp53 deficiency in tumour initiation. Consistent with this idea, analyses of 521 human endometrial carcinomas identified frequent mTORC1 pathway activation in type I as well as type II endometrial carcinoma subtypes. mTORC1 pathway activation and p53 expression or mutation status each independently predicted poor patient survival. We suggest that molecular alterations in p53 and the mTORC1 pathway play different roles in the initiation of the different endometrial cancer subtypes, but that combined p53 inactivation and mTORC1 pathway activation are unifying pathogenic features among histologically diverse subtypes of late stage aggressive endometrial tumours.
End-to-End Dexterous Manipulation with Deliberate Interactive Estimation.
Nicolas H. Hudson, Tom Howard, Jeremy Ma, Abhinandan Jain, Max Bajracharya, Steven Myint, Larry Matthies, Paul Backes, Paul Hebert, Thomas J. Fuchs and Joel Burdick.
IEEE International Conference on Robotics and Automation (ICRA), p. 2371-2378, 2012
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{hudson_end--end_2012,
    title = {End-to-{End} {Dexterous} {Manipulation} with {Deliberate} {Interactive} {Estimation}},
    url = {http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?reload=true&arnumber=6225101&contentType=Conference+Publications},
    doi = {10.1109/ICRA.2012.6225101},
    abstract = {This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.},
    booktitle = {{IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
    author = {Hudson, Nicolas H. and Howard, Tom and Ma, Jeremy and Jain, Abhinandan and Bajracharya, Max and Myint, Steven and Matthies, Larry and Backes, Paul and Hebert, Paul and Fuchs, Thomas J. and Burdick, Joel},
    year = {2012},
    pages = {2371--2378}
}
Download Endnote/RIS citation
TY - CONF
TI - End-to-End Dexterous Manipulation with Deliberate Interactive Estimation
AU - Hudson, Nicolas H.
AU - Howard, Tom
AU - Ma, Jeremy
AU - Jain, Abhinandan
AU - Bajracharya, Max
AU - Myint, Steven
AU - Matthies, Larry
AU - Backes, Paul
AU - Hebert, Paul
AU - Fuchs, Thomas J.
AU - Burdick, Joel
AB - This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.
C3 - IEEE International Conference on Robotics and Automation (ICRA)
DA - 2012///
PY - 2012
DO - 10.1109/ICRA.2012.6225101
SP - 2371
EP - 2378
UR - http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?reload=true&arnumber=6225101&contentType=Conference+Publications
ER -
This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.
Smart Cameras for Remote Science Survey.
David R. Thompson, William Abbey, Abigail Allwood, Dmitriy Bekker, Benjamin Bornstein, Nathalie A. Cabrol, Rebecca Castano, Tara Estlin, Thomas J. Fuchs and Kiri L. Wagstaff.
Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), 2012
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@inproceedings{thompson_smart_2012,
    title = {Smart {Cameras} for {Remote} {Science} {Survey}},
    url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.5807},
    abstract = {Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels- distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.},
    booktitle = {Proceedings of the 10th {International} {Symposium} on {Artificial} {Intelligence}, {Robotics} and {Automation} in {Space} (i-{SAIRAS})},
    author = {Thompson, David R. and Abbey, William and Allwood, Abigail and Bekker, Dmitriy and Bornstein, Benjamin and Cabrol, Nathalie A. and Castano, Rebecca and Estlin, Tara and Fuchs, Thomas J. and Wagstaff, Kiri L.},
    year = {2012},
}
Download Endnote/RIS citation
TY - CONF
TI - Smart Cameras for Remote Science Survey
AU - Thompson, David R.
AU - Abbey, William
AU - Allwood, Abigail
AU - Bekker, Dmitriy
AU - Bornstein, Benjamin
AU - Cabrol, Nathalie A.
AU - Castano, Rebecca
AU - Estlin, Tara
AU - Fuchs, Thomas J.
AU - Wagstaff, Kiri L.
AB - Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels- distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.
C3 - Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS)
DA - 2012///
PY - 2012
UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.5807
ER -
Communication with remote exploration spacecraft is often intermittent and bandwidth is highly constrained. Future missions could use onboard science data understanding to prioritize downlink of critical features [1], draft summary maps of visited terrain [2], or identify targets of opportunity for followup measurements [3]. We describe a generic approach to classify geologic surfaces for autonomous science operations, suitable for parallelized implementations in FPGA hardware. We map these surfaces with texture channels- distinctive numerical signatures that differentiate properties such as roughness, pavement coatings, regolith characteristics, sedimentary fabrics and differential outcrop weathering. This work describes our basic image analysis approach and reports an initial performance evaluation using surface images from the Mars Exploration Rovers. Future work will incorporate these methods into camera hardware for real-time processing.
Serum and Prostate Cancer Tissue Signatures of ERG Rearrangement Derived from Quantitative Analysis of the PTEN Conditional Knockout Mouse Proteome.
Niels J. Rupp, Igor Cima, Ralph Schiess, Peter J. Schüffler, Thomas J. Fuchs, Niklaus Frankhauser, Martin Kälin, Silke Gillessen, Ruedi Aebersold, Wilhelm Krek, Mark A. Rubin, Holger Moch and Peter J. Wild.
Symposium of the German Society for Pathology, 2012
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@inproceedings{rupp_serum_2012,
    title = {Serum and {Prostate} {Cancer} {Tissue} {Signatures} of {ERG} {Rearrangement} {Derived} from {Quantitative} {Analysis} of the {PTEN} {Conditional} {Knockout} {Mouse} {Proteome}},
    abstract = {Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers.
    Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement.
    Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41\% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort).
    Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.},
    booktitle = {Symposium of the {German} {Society} for {Pathology}},
    author = {Rupp, Niels J. and Cima, Igor and Schiess, Ralph and Sch\"uffler, Peter J. and Fuchs, Thomas J. and Frankhauser, Niklaus and K\"alin, Martin and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm and Rubin, Mark A. and Moch, Holger and Wild, Peter J.},
    year = {2012}
}
Download Endnote/RIS citation
TY - CONF
TI - Serum and Prostate Cancer Tissue Signatures of ERG Rearrangement Derived from Quantitative Analysis of the PTEN Conditional Knockout Mouse Proteome
AU - Rupp, Niels J.
AU - Cima, Igor
AU - Schiess, Ralph
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Frankhauser, Niklaus
AU - Kälin, Martin
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
AU - Rubin, Mark A.
AU - Moch, Holger
AU - Wild, Peter J.
AB - Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers.
Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement.
Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort).
Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.
C3 - Symposium of the German Society for Pathology
DA - 2012///
PY - 2012
ER -
Aims: Applying a systems biology approach to assess serum and tissue signatures of ERG rearrangement in patients with prostate cancer with the goal of determining downstream molecular targets for gene fusion cancers. Methods: Prostate tissue from a conditional PTEN knockout mouse model of prostate cancer was investigated, using selective enrichment of N-glycopeptides and mass spectrometry-based label-free quantification. Mouse tissue signatures were validated in sera and tissue of mice (n=12) and humans (n=105) by selected reaction monitoring (SRM), ELISA, and immunohistochemistry. ERG rearrangement status was assessed using fluorescence in situ hybridization (FISH) on two independent tissue microarray based prostatectomy cohorts (n=41, n=348). A Random forest model was trained and validated to identify serum and tissue signatures of ERG rearrangement. Results: A comprehensive PTEN dependent protein catalogue representing over 700 glycoproteins was established. TMPRSS2-ERG gene fusions occured in 41% (17/41) of analyzable prostate cancers of the training cohort. ERG dependent serum signatures could be found. Serum signatures were tested on prostate cancer tissue microarrays using immunohistochemistry (test cohort). Conclusions: This is the first study to demonstrate a proteomic signature of ERG rearrangement prostate cancer. Given the challenges of directly targeting ETS transcription factors, this study has potential clinical implications providing important insights into future targetable downstream pathways.
A High-Throughput Metabolomics Method to Predict High Concentration Cytotoxicity of Drugs from Low Concentration Profiles.
Stephanie Heux, Thomas J. Fuchs, Joachim Buhmann, Nicola Zamboni and Uwe Sauer.
Metabolomics, vol. 8, 3, p. 433-443, 2012
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@article{heux_high-throughput_2012,
    title = {A {High}-{Throughput} {Metabolomics} {Method} to {Predict} {High} {Concentration} {Cytotoxicity} of {Drugs} from {Low} {Concentration} {Profiles}},
    volume = {8},
    issn = {1573-3882},
    url = {http://dx.doi.org/10.1007/s11306-011-0386-0},
    doi = {10.1007/s11306-011-0386-0},
    number = {3},
    journal = {Metabolomics},
    author = {Heux, Stephanie and Fuchs, Thomas J. and Buhmann, Joachim and Zamboni, Nicola and Sauer, Uwe},
    year = {2012},
    keywords = {Biomedical and Life Sciences},
    pages = {433--443}
}
Download Endnote/RIS citation
TY - JOUR
TI - A High-Throughput Metabolomics Method to Predict High Concentration Cytotoxicity of Drugs from Low Concentration Profiles
AU - Heux, Stephanie
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim
AU - Zamboni, Nicola
AU - Sauer, Uwe
T2 - Metabolomics
DA - 2012///
PY - 2012
DO - 10.1007/s11306-011-0386-0
VL - 8
IS - 3
SP - 433
EP - 443
SN - 1573-3882
UR - http://dx.doi.org/10.1007/s11306-011-0386-0
KW - Biomedical and Life Sciences
ER -
TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting.
Peter J. Schueffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI, 2012
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@inproceedings{schueffler_tmarker:_2012,
    title = {{TMARKER}: {A} {User}-{Friendly} {Open}-{Source} {Assistance} for {Tma} {Grading} and {Cell} {Counting}},
    booktitle = {Histopathology {Image} {Analysis} ({HIMA}) {Workshop} at the 15th {International} {Conference} on {Medical} {Image} {Computing} and {Computer} {Assisted} {Intervention} {MICCAI}},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2012}
}
Download Endnote/RIS citation
TY - CONF
TI - TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
C3 - Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI
DA - 2012///
PY - 2012
ER -
A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma: A Large-Scale Tissue-Microarray Study with Two Independent Patient Cohorts.
Stefanie Meyer, Thomas J. Fuchs, Anja K. Bosserhoff, Ferdinand Hofstädter, Armin Pauer, Volker Roth, Joachim M. Buhmann, Ingrid Moll, Nikos Anagnostou, Johanna M. Brandner, Kristian Ikenberg, Holger Moch, Michael Landthaler, Thomas Vogt and Peter J. Wild.
PLoS ONE, vol. 7, 6, p. e38222, 2012
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@article{meyer_seven-marker_2012,
    title = {A {Seven}-{Marker} {Signature} and {Clinical} {Outcome} in {Malignant} {Melanoma}: {A} {Large}-{Scale} {Tissue}-{Microarray} {Study} with {Two} {Independent} {Patient} {Cohorts}},
    volume = {7},
    shorttitle = {A {Seven}-{Marker} {Signature} and {Clinical} {Outcome} in {Malignant} {Melanoma}},
    url = {http://dx.doi.org/10.1371/journal.pone.0038222},
    doi = {10.1371/journal.pone.0038222},
    abstract = {BackgroundCurrent staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment.Methods and FindingsUsing tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation, cell adhesion, signaling and metabolism. A marker selection procedure based on univariate Cox regression and multiple testing correction was employed to correlate the IHC expression data with the clinical follow-up (overall and recurrence-free survival). The model was thoroughly evaluated with two different cross validation experiments, a permutation test and a multivariate Cox regression analysis. In addition, the predictive power of the identified marker signature was validated on a second independent external test cohort (n = 225). A signature of seven biomarkers (Bax, Bcl-X, PTEN, COX-2, loss of β-Catenin, loss of MTAP, and presence of CD20 positive B-lymphocytes) was found to be an independent negative predictor for overall and recurrence-free survival in patients with MM. The seven-marker signature could also predict a high risk of disease recurrence in patients with localized primary MM stage pT1-2 (tumor thickness ≤2.00 mm). In particular, three of these markers (MTAP, COX-2, Bcl-X) were shown to offer direct therapeutic implications.ConclusionsThe seven-marker signature might serve as a prognostic tool enabling physicians to selectively triage, at the time of diagnosis, the subset of high recurrence risk stage I–II patients for adjuvant therapy. Selective treatment of those patients that are more likely to develop distant metastatic disease could potentially lower the burden of untreatable metastatic melanoma and revolutionize the therapeutic management of MM.},
    number = {6},
    urldate = {2016-01-03TZ},
    journal = {PLoS ONE},
    author = {Meyer, Stefanie and Fuchs, Thomas J. and Bosserhoff, Anja K. and Hofst\"adter, Ferdinand and Pauer, Armin and Roth, Volker and Buhmann, Joachim M. and Moll, Ingrid and Anagnostou, Nikos and Brandner, Johanna M. and Ikenberg, Kristian and Moch, Holger and Landthaler, Michael and Vogt, Thomas and Wild, Peter J.},
    year = {2012},
    pages = {e38222}
}
Download Endnote/RIS citation
TY - JOUR
TI - A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma: A Large-Scale Tissue-Microarray Study with Two Independent Patient Cohorts
AU - Meyer, Stefanie
AU - Fuchs, Thomas J.
AU - Bosserhoff, Anja K.
AU - Hofstädter, Ferdinand
AU - Pauer, Armin
AU - Roth, Volker
AU - Buhmann, Joachim M.
AU - Moll, Ingrid
AU - Anagnostou, Nikos
AU - Brandner, Johanna M.
AU - Ikenberg, Kristian
AU - Moch, Holger
AU - Landthaler, Michael
AU - Vogt, Thomas
AU - Wild, Peter J.
T2 - PLoS ONE
AB - BackgroundCurrent staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment.Methods and FindingsUsing tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation, cell adhesion, signaling and metabolism. A marker selection procedure based on univariate Cox regression and multiple testing correction was employed to correlate the IHC expression data with the clinical follow-up (overall and recurrence-free survival). The model was thoroughly evaluated with two different cross validation experiments, a permutation test and a multivariate Cox regression analysis. In addition, the predictive power of the identified marker signature was validated on a second independent external test cohort (n = 225). A signature of seven biomarkers (Bax, Bcl-X, PTEN, COX-2, loss of β-Catenin, loss of MTAP, and presence of CD20 positive B-lymphocytes) was found to be an independent negative predictor for overall and recurrence-free survival in patients with MM. The seven-marker signature could also predict a high risk of disease recurrence in patients with localized primary MM stage pT1-2 (tumor thickness ≤2.00 mm). In particular, three of these markers (MTAP, COX-2, Bcl-X) were shown to offer direct therapeutic implications.ConclusionsThe seven-marker signature might serve as a prognostic tool enabling physicians to selectively triage, at the time of diagnosis, the subset of high recurrence risk stage I–II patients for adjuvant therapy. Selective treatment of those patients that are more likely to develop distant metastatic disease could potentially lower the burden of untreatable metastatic melanoma and revolutionize the therapeutic management of MM.
DA - 2012///
PY - 2012
DO - 10.1371/journal.pone.0038222
DP - PLoS Journals
VL - 7
IS - 6
SP - e38222
J2 - PLoS ONE
ST - A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma
UR - http://dx.doi.org/10.1371/journal.pone.0038222
Y2 - 2016/01/03/T15:45:02Z
ER -
BackgroundCurrent staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment.Methods and FindingsUsing tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation, cell adhesion, signaling and metabolism. A marker selection procedure based on univariate Cox regression and multiple testing correction was employed to correlate the IHC expression data with the clinical follow-up (overall and recurrence-free survival). The model was thoroughly evaluated with two different cross validation experiments, a permutation test and a multivariate Cox regression analysis. In addition, the predictive power of the identified marker signature was validated on a second independent external test cohort (n = 225). A signature of seven biomarkers (Bax, Bcl-X, PTEN, COX-2, loss of β-Catenin, loss of MTAP, and presence of CD20 positive B-lymphocytes) was found to be an independent negative predictor for overall and recurrence-free survival in patients with MM. The seven-marker signature could also predict a high risk of disease recurrence in patients with localized primary MM stage pT1-2 (tumor thickness ≤2.00 mm). In particular, three of these markers (MTAP, COX-2, Bcl-X) were shown to offer direct therapeutic implications.ConclusionsThe seven-marker signature might serve as a prognostic tool enabling physicians to selectively triage, at the time of diagnosis, the subset of high recurrence risk stage I–II patients for adjuvant therapy. Selective treatment of those patients that are more likely to develop distant metastatic disease could potentially lower the burden of untreatable metastatic melanoma and revolutionize the therapeutic management of MM.
Combined Shape, Appearance and Silhouette for Simultaneous Manipulator and Object Tracking.
Paul Hebert, Nicolas Hudson, Jeremy Ma, Thomas Howard, Thomas J. Fuchs, Max Bajracharya and Joel Burdick.
IEEE International Conference on Robotics and Automation (ICRA), p. 2405-2412, 2012
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@inproceedings{hebert_combined_2012,
    title = {Combined {Shape}, {Appearance} and {Silhouette} for {Simultaneous} {Manipulator} and {Object} {Tracking}},
    url = {http://robotics.caltech.edu/wiki/images/d/d3/HebertICRA12.pdf},
    doi = {10.1109/ICRA.2012.6225084},
    booktitle = {{IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
    author = {Hebert, Paul and Hudson, Nicolas and Ma, Jeremy and Howard, Thomas and Fuchs, Thomas J. and Bajracharya, Max and Burdick, Joel},
    year = {2012},
    pages = {2405--2412}
}
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TY - CONF
TI - Combined Shape, Appearance and Silhouette for Simultaneous Manipulator and Object Tracking
AU - Hebert, Paul
AU - Hudson, Nicolas
AU - Ma, Jeremy
AU - Howard, Thomas
AU - Fuchs, Thomas J.
AU - Bajracharya, Max
AU - Burdick, Joel
C3 - IEEE International Conference on Robotics and Automation (ICRA)
DA - 2012///
PY - 2012
DO - 10.1109/ICRA.2012.6225084
SP - 2405
EP - 2412
UR - http://robotics.caltech.edu/wiki/images/d/d3/HebertICRA12.pdf
ER -
TextureCam: Autonomous Image Analysis for Astrobiology Survey.
David R. Thompson, Abigail Allwood, Dmitriy Bekker, Nathalie A. Cabrol, Tara Estlin, Thomas J. Fuchs and Kiri L. Wagstaff.
43rd Lunar and Planetary Science Conference, vol. 43, p. 1659, 2012
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@inproceedings{thompson_texturecam:_2012,
    title = {{TextureCam}: {Autonomous} {Image} {Analysis} for {Astrobiology} {Survey}},
    volume = {43},
    url = {http://adsabs.harvard.edu/abs/2012LPI....43.1659T},
    booktitle = {43rd {Lunar} and {Planetary} {Science} {Conference}},
    author = {Thompson, David R. and Allwood, Abigail and Bekker, Dmitriy and Cabrol, Nathalie A. and Estlin, Tara and Fuchs, Thomas J. and Wagstaff, Kiri L.},
    year = {2012},
    pages = {1659}
}
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TY - CONF
TI - TextureCam: Autonomous Image Analysis for Astrobiology Survey
AU - Thompson, David R.
AU - Allwood, Abigail
AU - Bekker, Dmitriy
AU - Cabrol, Nathalie A.
AU - Estlin, Tara
AU - Fuchs, Thomas J.
AU - Wagstaff, Kiri L.
C3 - 43rd Lunar and Planetary Science Conference
DA - 2012///
PY - 2012
VL - 43
SP - 1659
UR - http://adsabs.harvard.edu/abs/2012LPI....43.1659T
ER -
Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer.
Igor Cima, Ralph Schiess, Peter Wild, Martin Kaelin, Peter Schueffler, Vinzenz Lange, Paola Picotti, Reto Ossola, Arnoud Templeton, Olga Schubert, Thomas J. Fuchs, Thomas Leippold, Stephen Wyler, Jens Zehetner, Wolfram Jochum, Joachim Buhmann, Thomas Cerny, Holger Moch, Silke Gillessen, Ruedi Aebersold and Wilhelm Krek.
PNAS: Proceedings of the National Academy of Sciences, vol. 108, 8, p. 3342-3347, 2011
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@article{cima_cancer_2011,
    title = {Cancer {Genetics}-{Guided} {Discovery} of {Serum} {Biomarker} {Signatures} for {Diagnosis} and {Prognosis} of {Prostate} {Cancer}},
    volume = {108},
    url = {http://www.pnas.org/content/108/8/3342.abstract},
    doi = {10.1073/pnas.1013699108},
    abstract = {A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.},
    number = {8},
    journal = {PNAS: Proceedings of the National Academy of Sciences},
    author = {Cima, Igor and Schiess, Ralph and Wild, Peter and Kaelin, Martin and Schueffler, Peter and Lange, Vinzenz and Picotti, Paola and Ossola, Reto and Templeton, Arnoud and Schubert, Olga and Fuchs, Thomas J. and Leippold, Thomas and Wyler, Stephen and Zehetner, Jens and Jochum, Wolfram and Buhmann, Joachim and Cerny, Thomas and Moch, Holger and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm},
    year = {2011},
    pages = {3342--3347}
}
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TY - JOUR
TI - Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer
AU - Cima, Igor
AU - Schiess, Ralph
AU - Wild, Peter
AU - Kaelin, Martin
AU - Schueffler, Peter
AU - Lange, Vinzenz
AU - Picotti, Paola
AU - Ossola, Reto
AU - Templeton, Arnoud
AU - Schubert, Olga
AU - Fuchs, Thomas J.
AU - Leippold, Thomas
AU - Wyler, Stephen
AU - Zehetner, Jens
AU - Jochum, Wolfram
AU - Buhmann, Joachim
AU - Cerny, Thomas
AU - Moch, Holger
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
T2 - PNAS: Proceedings of the National Academy of Sciences
AB - A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
DA - 2011///
PY - 2011
DO - 10.1073/pnas.1013699108
VL - 108
IS - 8
SP - 3342
EP - 3347
UR - http://www.pnas.org/content/108/8/3342.abstract
ER -
A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
Aerosols Transmit Prions to Immunocompetent and Immunodeficient Mice.
Johannes Haybaeck, Mathias Heikenwalder, Britta Klevenz, Petra Schwarz, Ilan Margalith, Claire Bridel, Kirsten Mertz, Elizabeta Zirdum, Benjamin Petsch, Thomas J. Fuchs, Lothar Stitz and Adriano Aguzzi.
PLoS Pathog, vol. 7, 1, p. e1001257, 2011
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@article{haybaeck_aerosols_2011,
    title = {Aerosols {Transmit} {Prions} to {Immunocompetent} and {Immunodeficient} {Mice}},
    volume = {7},
    url = {http://dx.doi.org/10.1371/journal.ppat.1001257},
    doi = {10.1371/journal.ppat.1001257},
    abstract = {Author Summary
    Prions, which are the cause of fatal neurodegenerative disorders termed transmissible spongiform encephalopathies (TSEs), can be experimentally or naturally transmitted via prion-contaminated food, blood, milk, saliva, feces and urine. Here we demonstrate that prions can be transmitted through aerosols in mice. This also occurs in the absence of immune cells as demonstrated by experiments with mice lacking B-, T-, follicular dendritic cells (FDCs), lymphotoxin signaling or with complement-deficient mice. Therefore, a functionally intact immune system is not strictly needed for aerogenic prion infection. These results suggest that current biosafety guidelines applied in diagnostic and scientific laboratories ought to include prion aerosols as a potential vector for prion infection.},
    number = {1},
    urldate = {2016-01-03TZ},
    journal = {PLoS Pathog},
    author = {Haybaeck, Johannes and Heikenwalder, Mathias and Klevenz, Britta and Schwarz, Petra and Margalith, Ilan and Bridel, Claire and Mertz, Kirsten and Zirdum, Elizabeta and Petsch, Benjamin and Fuchs, Thomas J. and Stitz, Lothar and Aguzzi, Adriano},
    year = {2011},
    pages = {e1001257}
}
Download Endnote/RIS citation
TY - JOUR
TI - Aerosols Transmit Prions to Immunocompetent and Immunodeficient Mice
AU - Haybaeck, Johannes
AU - Heikenwalder, Mathias
AU - Klevenz, Britta
AU - Schwarz, Petra
AU - Margalith, Ilan
AU - Bridel, Claire
AU - Mertz, Kirsten
AU - Zirdum, Elizabeta
AU - Petsch, Benjamin
AU - Fuchs, Thomas J.
AU - Stitz, Lothar
AU - Aguzzi, Adriano
T2 - PLoS Pathog
AB - Author Summary
Prions, which are the cause of fatal neurodegenerative disorders termed transmissible spongiform encephalopathies (TSEs), can be experimentally or naturally transmitted via prion-contaminated food, blood, milk, saliva, feces and urine. Here we demonstrate that prions can be transmitted through aerosols in mice. This also occurs in the absence of immune cells as demonstrated by experiments with mice lacking B-, T-, follicular dendritic cells (FDCs), lymphotoxin signaling or with complement-deficient mice. Therefore, a functionally intact immune system is not strictly needed for aerogenic prion infection. These results suggest that current biosafety guidelines applied in diagnostic and scientific laboratories ought to include prion aerosols as a potential vector for prion infection.
DA - 2011///
PY - 2011
DO - 10.1371/journal.ppat.1001257
DP - PLoS Journals
VL - 7
IS - 1
SP - e1001257
J2 - PLoS Pathog
UR - http://dx.doi.org/10.1371/journal.ppat.1001257
Y2 - 2016/01/03/T16:08:54Z
ER -
Author Summary Prions, which are the cause of fatal neurodegenerative disorders termed transmissible spongiform encephalopathies (TSEs), can be experimentally or naturally transmitted via prion-contaminated food, blood, milk, saliva, feces and urine. Here we demonstrate that prions can be transmitted through aerosols in mice. This also occurs in the absence of immune cells as demonstrated by experiments with mice lacking B-, T-, follicular dendritic cells (FDCs), lymphotoxin signaling or with complement-deficient mice. Therefore, a functionally intact immune system is not strictly needed for aerogenic prion infection. These results suggest that current biosafety guidelines applied in diagnostic and scientific laboratories ought to include prion aerosols as a potential vector for prion infection.
Computational Pathology: Challenges and Promises for Tissue Analysis.
Thomas J. Fuchs and Joachim M. Buhmann.
Computerized Medical Imaging and Graphics, vol. 35, 7–8, p. 515-530, 2011
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@article{fuchs_computational_2011,
    title = {Computational {Pathology}: {Challenges} and {Promises} for {Tissue} {Analysis}},
    volume = {35},
    issn = {0895-6111},
    shorttitle = {Computational pathology},
    url = {http://www.sciencedirect.com/science/article/pii/S0895611111000383},
    doi = {10.1016/j.compmedimag.2011.02.006},
    abstract = {The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.},
    number = {7–8},
    urldate = {2012-03-08TZ},
    journal = {Computerized Medical Imaging and Graphics},
    author = {Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2011},
    keywords = {Cancer research, Computational pathology, Medical imaging, Survival statistics, Whole, Whole slide imaging, imaging, machine learning, slide},
    pages = {515--530}
}
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TY - JOUR
TI - Computational Pathology: Challenges and Promises for Tissue Analysis
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
T2 - Computerized Medical Imaging and Graphics
AB - The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
DA - 2011///
PY - 2011
DO - 10.1016/j.compmedimag.2011.02.006
DP - ScienceDirect
VL - 35
IS - 7–8
SP - 515
EP - 530
SN - 0895-6111
ST - Computational pathology
UR - http://www.sciencedirect.com/science/article/pii/S0895611111000383
Y2 - 2012/03/08/T15:27:48Z
KW - Cancer research
KW - Computational pathology
KW - Medical imaging
KW - Survival statistics
KW - Whole
KW - Whole slide imaging
KW - imaging
KW - machine learning
KW - slide
ER -
The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data.
Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs and Volker Roth.
Proceedings of the 27th International Conference on Machine Learning, p. 1111-1118, ICML'10, 2010
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@inproceedings{vogt_translation-invariant_2010,
    series = {{ICML}'10},
    title = {The {Translation}-invariant {Wishart}-{Dirichlet} {Process} for {Clustering} {Distance} {Data}},
    url = {http://www.icml2010.org/papers/248.pdf},
    booktitle = {Proceedings of the 27th {International} {Conference} on {Machine} {Learning}},
    author = {Vogt, Julia E. and Prabhakaran, Sandhya and Fuchs, Thomas J. and Roth, Volker},
    year = {2010},
    pages = {1111--1118}
}
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TY - CONF
TI - The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data
AU - Vogt, Julia E.
AU - Prabhakaran, Sandhya
AU - Fuchs, Thomas J.
AU - Roth, Volker
T3 - ICML'10
C3 - Proceedings of the 27th International Conference on Machine Learning
DA - 2010///
PY - 2010
SP - 1111
EP - 1118
UR - http://www.icml2010.org/papers/248.pdf
ER -
Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images.
Verena Kaynig, Thomas J. Fuchs and Joachim M. Buhmann.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 0, p. 2902-2909, IEEE Computer Society, ISBN 978-1-4244-6984-0, 2010
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@inproceedings{kaynig_neuron_2010,
    address = {Los Alamitos, CA, USA},
    title = {Neuron {Geometry} {Extraction} by {Perceptual} {Grouping} in {ssTEM} {Images}},
    volume = {0},
    isbn = {978-1-4244-6984-0},
    url = {http://www.computer.org/portal/web/csdl/doi/10.1109/CVPR.2010.5540029},
    doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5540029},
    booktitle = {{IEEE} {Computer} {Society} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
    publisher = {IEEE Computer Society},
    author = {Kaynig, Verena and Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2010},
    pages = {2902--2909}
}
Download Endnote/RIS citation
TY - CONF
TI - Neuron Geometry Extraction by Perceptual Grouping in ssTEM Images
AU - Kaynig, Verena
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
C1 - Los Alamitos, CA, USA
C3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DA - 2010///
PY - 2010
DO - http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5540029
VL - 0
SP - 2902
EP - 2909
PB - IEEE Computer Society
SN - 978-1-4244-6984-0
UR - http://www.computer.org/portal/web/csdl/doi/10.1109/CVPR.2010.5540029
ER -
TAK1 Suppresses a NEMO-Dependent but NF-kappaB-Independent Pathway to Liver Cancer.
Kira Bettermann, Mihael Vucur, Johannes Haybaeck, Christiane Koppe, Joern Janssen, Felix Heymann, Achim Weber, Ralf Weiskirchen, Christian Liedtke, Nikolaus Gassler, Michael Mueller, Rita de Vos, Monika Julia Wolf, Yannick Boege, Gitta Maria Seleznik, Nicolas Zeller, Daniel Erny, Thomas J. Fuchs, Stefan Zoller, Stefano Cairo, Marie-Annick Buendia, Marco Prinz, Shizuo Akira, Frank Tacke, Mathias Heikenwaelder, Christian Trautwein and Tom Luedde.
Cancer Cell, vol. 17, 5, p. 481–496, 2010
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@article{bettermann_tak1_2010,
    title = {{TAK}1 {Suppresses} a {NEMO}-{Dependent} but {NF}-{kappaB}-{Independent} {Pathway} to {Liver} {Cancer}},
    volume = {17},
    url = {http://www.ncbi.nlm.nih.gov/pubmed/20478530},
    number = {5},
    journal = {Cancer Cell},
    author = {Bettermann, Kira and Vucur, Mihael and Haybaeck, Johannes and Koppe, Christiane and Janssen, Joern and Heymann, Felix and Weber, Achim and Weiskirchen, Ralf and Liedtke, Christian and Gassler, Nikolaus and Mueller, Michael and de Vos, Rita and Wolf, Monika Julia and Boege, Yannick and Seleznik, Gitta Maria and Zeller, Nicolas and Erny, Daniel and Fuchs, Thomas J. and Zoller, Stefan and Cairo, Stefano and Buendia, Marie-Annick and Prinz, Marco and Akira, Shizuo and Tacke, Frank and Heikenwaelder, Mathias and Trautwein, Christian and Luedde, Tom},
    year = {2010},
    pages = {481--496}
}
Download Endnote/RIS citation
TY - JOUR
TI - TAK1 Suppresses a NEMO-Dependent but NF-kappaB-Independent Pathway to Liver Cancer
AU - Bettermann, Kira
AU - Vucur, Mihael
AU - Haybaeck, Johannes
AU - Koppe, Christiane
AU - Janssen, Joern
AU - Heymann, Felix
AU - Weber, Achim
AU - Weiskirchen, Ralf
AU - Liedtke, Christian
AU - Gassler, Nikolaus
AU - Mueller, Michael
AU - de Vos, Rita
AU - Wolf, Monika Julia
AU - Boege, Yannick
AU - Seleznik, Gitta Maria
AU - Zeller, Nicolas
AU - Erny, Daniel
AU - Fuchs, Thomas J.
AU - Zoller, Stefan
AU - Cairo, Stefano
AU - Buendia, Marie-Annick
AU - Prinz, Marco
AU - Akira, Shizuo
AU - Tacke, Frank
AU - Heikenwaelder, Mathias
AU - Trautwein, Christian
AU - Luedde, Tom
T2 - Cancer Cell
DA - 2010///
PY - 2010
VL - 17
IS - 5
SP - 481
EP - 496
UR - http://www.ncbi.nlm.nih.gov/pubmed/20478530
ER -
Infinite Mixture-of-Experts Model for Sparse Survival Regression with Application to Breast Cancer.
Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl, Joachim M. Buhmann and Volker Roth.
BMC Bioinformatics, vol. 11, 8, p. S8, 2010
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@article{raman_infinite_2010,
    title = {Infinite {Mixture}-of-{Experts} {Model} for {Sparse} {Survival} {Regression} with {Application} to {Breast} {Cancer}},
    volume = {11},
    issn = {1471-2105},
    url = {http://dx.doi.org/10.1186/1471-2105-11-S8-S8},
    doi = {10.1186/1471-2105-11-S8-S8},
    abstract = {We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.},
    number = {8},
    urldate = {2016-01-03TZ},
    journal = {BMC Bioinformatics},
    author = {Raman, Sudhir and Fuchs, Thomas J. and Wild, Peter J. and Dahl, Edgar and Buhmann, Joachim M. and Roth, Volker},
    year = {2010},
    pages = {S8}
}
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TY - JOUR
TI - Infinite Mixture-of-Experts Model for Sparse Survival Regression with Application to Breast Cancer
AU - Raman, Sudhir
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AU - Dahl, Edgar
AU - Buhmann, Joachim M.
AU - Roth, Volker
T2 - BMC Bioinformatics
AB - We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.
DA - 2010///
PY - 2010
DO - 10.1186/1471-2105-11-S8-S8
DP - BioMed Central
VL - 11
IS - 8
SP - S8
J2 - BMC Bioinformatics
SN - 1471-2105
UR - http://dx.doi.org/10.1186/1471-2105-11-S8-S8
Y2 - 2016/01/03/T16:02:59Z
ER -
We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.
Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data.
Verena Kaynig, Thomas J. Fuchs and Joachim M. Buhmann.
Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II, p. 209–216, MICCAI'10, Springer-Verlag, Berlin, Heidelberg, ISBN 3-642-15744-0 978-3-642-15744-8, 2010
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@inproceedings{kaynig_geometrical_2010,
    address = {Beijing, China},
    series = {{MICCAI}'10},
    title = {Geometrical {Consistent} 3D {Tracing} of {Neuronal} {Processes} in {ssTEM} {Data}},
    isbn = {3-642-15744-0 978-3-642-15744-8},
    url = {http://portal.acm.org/citation.cfm?id=1928047.1928075},
    booktitle = {Proceedings of the 13th international conference on {Medical} image computing and computer-assisted intervention: {Part} {II}},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Kaynig, Verena and Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2010},
    pages = {209--216}
}
Download Endnote/RIS citation
TY - CONF
TI - Geometrical Consistent 3D Tracing of Neuronal Processes in ssTEM Data
AU - Kaynig, Verena
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
T3 - MICCAI'10
C1 - Beijing, China
C3 - Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
DA - 2010///
PY - 2010
SP - 209
EP - 216
PB - Springer-Verlag, Berlin, Heidelberg
SN - 3-642-15744-0 978-3-642-15744-8
UR - http://portal.acm.org/citation.cfm?id=1928047.1928075
ER -
Effect of Reader Experience on Variability, Evaluation Time and Accuracy of Coronary Plaque Detection with Computed Tomography Coronary Angiography.
Stefan Saur, Hatem Alkadhi, Paul Stolzmann, Stephan Baumueller, Sebastian Leschka, Hans Scheffel, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Philippe Cattin.
European Radiology, vol. 20, 7, p. 1599-1606, 2010
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@article{saur_effect_2010,
    title = {Effect of {Reader} {Experience} on {Variability}, {Evaluation} {Time} and {Accuracy} of {Coronary} {Plaque} {Detection} with {Computed} {Tomography} {Coronary} {Angiography}},
    volume = {20},
    issn = {0938-7994},
    url = {http://dx.doi.org/10.1007/s00330-009-1709-7},
    doi = {10.1007/s00330-009-1709-7},
    number = {7},
    journal = {European Radiology},
    author = {Saur, Stefan and Alkadhi, Hatem and Stolzmann, Paul and Baumueller, Stephan and Leschka, Sebastian and Scheffel, Hans and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Cattin, Philippe},
    year = {2010},
    keywords = {Medicine},
    pages = {1599--1606}
}
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TY - JOUR
TI - Effect of Reader Experience on Variability, Evaluation Time and Accuracy of Coronary Plaque Detection with Computed Tomography Coronary Angiography
AU - Saur, Stefan
AU - Alkadhi, Hatem
AU - Stolzmann, Paul
AU - Baumueller, Stephan
AU - Leschka, Sebastian
AU - Scheffel, Hans
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Cattin, Philippe
T2 - European Radiology
DA - 2010///
PY - 2010
DO - 10.1007/s00330-009-1709-7
VL - 20
IS - 7
SP - 1599
EP - 1606
SN - 0938-7994
UR - http://dx.doi.org/10.1007/s00330-009-1709-7
KW - Medicine
ER -
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma.
Peter J. Schueffler, Thomas J. Fuchs, Cheng Soon Ong, Volker Roth and Joachim M. Buhmann.
Proceedings of the 32nd DAGM conference on Pattern recognition, p. 202–211, Springer-Verlag, Berlin, Heidelberg, ISBN 3-642-15985-0 978-3-642-15985-5, 2010
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@inproceedings{schueffler_computational_2010,
    address = {Darmstadt, Germany},
    title = {Computational {TMA} {Analysis} and {Cell} {Nucleus} {Classification} of {Renal} {Cell} {Carcinoma}},
    isbn = {3-642-15985-0 978-3-642-15985-5},
    url = {http://portal.acm.org/citation.cfm?id=1926258.1926281},
    abstract = {We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.},
    booktitle = {Proceedings of the 32nd {DAGM} conference on {Pattern} recognition},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng Soon and Roth, Volker and Buhmann, Joachim M.},
    year = {2010},
    pages = {202--211}
}
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TY - CONF
TI - Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng Soon
AU - Roth, Volker
AU - Buhmann, Joachim M.
AB - We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
C1 - Darmstadt, Germany
C3 - Proceedings of the 32nd DAGM conference on Pattern recognition
DA - 2010///
PY - 2010
SP - 202
EP - 211
PB - Springer-Verlag, Berlin, Heidelberg
SN - 3-642-15985-0 978-3-642-15985-5
UR - http://portal.acm.org/citation.cfm?id=1926258.1926281
ER -
We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped to machine learning challenges. We investigate three of these tasks, namely nuclei segmentation, nuclei classification and staining estimation. We argue for a holistic view of the processing pipeline, as it is not obvious whether performance improvements at individual steps improve overall accuracy. The experimental results show that classification accuracy, which is comparable to trained human experts, can be achieved by using support vector machines (SVM) with appropriate kernels. Furthermore, we provide evidence that the shape of cell nuclei increases the classification performance. Most importantly, these improvements in classification accuracy result in corresponding improvements for the medically relevant estimation of immunohistochemical staining.
Detection of Urothelial Bladder Cancer Cells in Voided Urine Can Be Improved by a Combination of Cytology and Standardized Microsatellite Analysis.
Peter J. Wild, Thomas J. Fuchs, Robert Stoehr, Dieter Zimmermann, Simona Frigerio, Barbara Padberg, Inbal Steiner, Ellen C. Zwarthoff, Maximilian Burger, Stefan Denzinger, Ferdinand Hofstaedter, Glen Kristiansen, Thomas Hermanns, Hans-Helge Seifert, Maurizio Provenzano, Tullio Sulser, Volker Roth, Joachim M. Buhmann, Holger Moch and Arndt Hartmann.
Cancer Epidemiol Biomarkers Prev, vol. 18, 6, p. 1798-1806, 2009
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@article{wild_detection_2009,
    title = {Detection of {Urothelial} {Bladder} {Cancer} {Cells} in {Voided} {Urine} {Can} {Be} {Improved} by a {Combination} of {Cytology} and {Standardized} {Microsatellite} {Analysis}},
    volume = {18},
    url = {http://cebp.aacrjournals.org/cgi/content/abstract/18/6/1798},
    doi = {10.1158/1055-9965.EPI-09-0099},
    abstract = {Purpose: To evaluate molecular and immunohistochemical markers to develop a molecular grading of urothelial bladder cancer and to test these markers in voided urine samples.
    Experimental Design: 255 consecutive biopsies from primary bladder cancer patients were evaluated on a tissue microarray. The clinical parameters gender, age, adjacent carcinoma in situ, and multifocality were collected. UroVysion fluorescence in situ hybridization (FISH) was done. Expression of cytokeratin 20, MIB1, and TP53 was analyzed by immunohistochemistry. Fibroblast growth factor receptor 3 (FGFR3) status was studied by SNaPshot mutation detection. Results were correlated with clinical outcome by Cox regression analysis. To assess the predictive power of different predictor subsets to detect high grade and tumor invasion, logistic regression models were learned. Additionally, voided urine samples of 119 patients were investigated. After cytologic examination, urine samples were matched with their biopsies and analyzed for loss of heterozygosity (LOH), FGFR3 mutation, polysomy, and p16 deletion using UroVysion FISH. Receiver operator characteristic curves for various predictor subsets were plotted.
    Results: In biopsies, high grade and solid growth pattern were independent prognostic factors for overall survival. A model consisting of UroVysion FISH and FGFR3 status (FISH + FGFR3) predicted high grade significantly better compared with a recently proposed molecular grade (MIB1 + FGFR3). In voided urine, the combination of cytology with LOH analysis (CYTO + LOH) reached the highest diagnostic accuracy for the detection of bladder cancer cells and performed better than cytology alone (sensitivity of 88.2\% and specificity of 97.1\%).
    Conclusions: The combination of cytology with LOH analysis could reduce unpleasant cystoscopies for bladder cancer patients.},
    number = {6},
    journal = {Cancer Epidemiol Biomarkers Prev},
    author = {Wild, Peter J. and Fuchs, Thomas J. and Stoehr, Robert and Zimmermann, Dieter and Frigerio, Simona and Padberg, Barbara and Steiner, Inbal and Zwarthoff, Ellen C. and Burger, Maximilian and Denzinger, Stefan and Hofstaedter, Ferdinand and Kristiansen, Glen and Hermanns, Thomas and Seifert, Hans-Helge and Provenzano, Maurizio and Sulser, Tullio and Roth, Volker and Buhmann, Joachim M. and Moch, Holger and Hartmann, Arndt},
    year = {2009},
    pages = {1798--1806}
}
Download Endnote/RIS citation
TY - JOUR
TI - Detection of Urothelial Bladder Cancer Cells in Voided Urine Can Be Improved by a Combination of Cytology and Standardized Microsatellite Analysis
AU - Wild, Peter J.
AU - Fuchs, Thomas J.
AU - Stoehr, Robert
AU - Zimmermann, Dieter
AU - Frigerio, Simona
AU - Padberg, Barbara
AU - Steiner, Inbal
AU - Zwarthoff, Ellen C.
AU - Burger, Maximilian
AU - Denzinger, Stefan
AU - Hofstaedter, Ferdinand
AU - Kristiansen, Glen
AU - Hermanns, Thomas
AU - Seifert, Hans-Helge
AU - Provenzano, Maurizio
AU - Sulser, Tullio
AU - Roth, Volker
AU - Buhmann, Joachim M.
AU - Moch, Holger
AU - Hartmann, Arndt
T2 - Cancer Epidemiol Biomarkers Prev
AB - Purpose: To evaluate molecular and immunohistochemical markers to develop a molecular grading of urothelial bladder cancer and to test these markers in voided urine samples.
Experimental Design: 255 consecutive biopsies from primary bladder cancer patients were evaluated on a tissue microarray. The clinical parameters gender, age, adjacent carcinoma in situ, and multifocality were collected. UroVysion fluorescence in situ hybridization (FISH) was done. Expression of cytokeratin 20, MIB1, and TP53 was analyzed by immunohistochemistry. Fibroblast growth factor receptor 3 (FGFR3) status was studied by SNaPshot mutation detection. Results were correlated with clinical outcome by Cox regression analysis. To assess the predictive power of different predictor subsets to detect high grade and tumor invasion, logistic regression models were learned. Additionally, voided urine samples of 119 patients were investigated. After cytologic examination, urine samples were matched with their biopsies and analyzed for loss of heterozygosity (LOH), FGFR3 mutation, polysomy, and p16 deletion using UroVysion FISH. Receiver operator characteristic curves for various predictor subsets were plotted.
Results: In biopsies, high grade and solid growth pattern were independent prognostic factors for overall survival. A model consisting of UroVysion FISH and FGFR3 status (FISH + FGFR3) predicted high grade significantly better compared with a recently proposed molecular grade (MIB1 + FGFR3). In voided urine, the combination of cytology with LOH analysis (CYTO + LOH) reached the highest diagnostic accuracy for the detection of bladder cancer cells and performed better than cytology alone (sensitivity of 88.2% and specificity of 97.1%).
Conclusions: The combination of cytology with LOH analysis could reduce unpleasant cystoscopies for bladder cancer patients.
DA - 2009///
PY - 2009
DO - 10.1158/1055-9965.EPI-09-0099
VL - 18
IS - 6
SP - 1798
EP - 1806
UR - http://cebp.aacrjournals.org/cgi/content/abstract/18/6/1798
ER -
Purpose: To evaluate molecular and immunohistochemical markers to develop a molecular grading of urothelial bladder cancer and to test these markers in voided urine samples. Experimental Design: 255 consecutive biopsies from primary bladder cancer patients were evaluated on a tissue microarray. The clinical parameters gender, age, adjacent carcinoma in situ, and multifocality were collected. UroVysion fluorescence in situ hybridization (FISH) was done. Expression of cytokeratin 20, MIB1, and TP53 was analyzed by immunohistochemistry. Fibroblast growth factor receptor 3 (FGFR3) status was studied by SNaPshot mutation detection. Results were correlated with clinical outcome by Cox regression analysis. To assess the predictive power of different predictor subsets to detect high grade and tumor invasion, logistic regression models were learned. Additionally, voided urine samples of 119 patients were investigated. After cytologic examination, urine samples were matched with their biopsies and analyzed for loss of heterozygosity (LOH), FGFR3 mutation, polysomy, and p16 deletion using UroVysion FISH. Receiver operator characteristic curves for various predictor subsets were plotted. Results: In biopsies, high grade and solid growth pattern were independent prognostic factors for overall survival. A model consisting of UroVysion FISH and FGFR3 status (FISH + FGFR3) predicted high grade significantly better compared with a recently proposed molecular grade (MIB1 + FGFR3). In voided urine, the combination of cytology with LOH analysis (CYTO + LOH) reached the highest diagnostic accuracy for the detection of bladder cancer cells and performed better than cytology alone (sensitivity of 88.2% and specificity of 97.1%). Conclusions: The combination of cytology with LOH analysis could reduce unpleasant cystoscopies for bladder cancer patients.
Nuclear Detection of Y-Box Protein-1 (YB-1) Closely Associates with Progesterone Receptor Negativity and Is a Strong Adverse Survival Factor in Human Breast Cancer.
Edgar Dahl, Abdelaziz En-Nia, Frank Wiesmann, Renate Krings, Sonja Djudjaj, Elisabeth Breuer, Thomas J. Fuchs, Peter Wild, Arndt Hartmann, Sandra Dunn and Peter Mertens.
BMC Cancer, vol. 9, 1, p. 410, 2009
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@article{dahl_nuclear_2009,
    title = {Nuclear {Detection} of {Y}-{Box} {Protein}-1 ({YB}-1) {Closely} {Associates} with {Progesterone} {Receptor} {Negativity} and {Is} a {Strong} {Adverse} {Survival} {Factor} in {Human} {Breast} {Cancer}},
    volume = {9},
    issn = {1471-2407},
    url = {http://www.biomedcentral.com/1471-2407/9/410},
    doi = {10.1186/1471-2407-9-410},
    number = {1},
    journal = {BMC Cancer},
    author = {Dahl, Edgar and En-Nia, Abdelaziz and Wiesmann, Frank and Krings, Renate and Djudjaj, Sonja and Breuer, Elisabeth and Fuchs, Thomas J. and Wild, Peter and Hartmann, Arndt and Dunn, Sandra and Mertens, Peter},
    year = {2009},
    pages = {410}
}
Download Endnote/RIS citation
TY - JOUR
TI - Nuclear Detection of Y-Box Protein-1 (YB-1) Closely Associates with Progesterone Receptor Negativity and Is a Strong Adverse Survival Factor in Human Breast Cancer
AU - Dahl, Edgar
AU - En-Nia, Abdelaziz
AU - Wiesmann, Frank
AU - Krings, Renate
AU - Djudjaj, Sonja
AU - Breuer, Elisabeth
AU - Fuchs, Thomas J.
AU - Wild, Peter
AU - Hartmann, Arndt
AU - Dunn, Sandra
AU - Mertens, Peter
T2 - BMC Cancer
DA - 2009///
PY - 2009
DO - 10.1186/1471-2407-9-410
VL - 9
IS - 1
SP - 410
SN - 1471-2407
UR - http://www.biomedcentral.com/1471-2407/9/410
ER -
Prediction Rules for the Detection of Coronary Artery Plaques: Evidence from Cardiac CT.
Stefan C. Saur, Philippe C. Cattin, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Hatem Alkadhi.
Investigative Radiology, vol. 44, 8, p. 483-490, 2009
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{saur_prediction_2009,
    title = {Prediction {Rules} for the {Detection} of {Coronary} {Artery} {Plaques}: {Evidence} from {Cardiac} {CT}},
    volume = {44},
    url = {http://journals.lww.com/investigativeradiology/Abstract/2009/08000/Prediction_Rules_for_the_Detection_of_Coronary.8.aspx},
    doi = {http://dx.doi.org/10.1097/RLI.0b013e3181a8afc4},
    abstract = {Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques.
    Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining.
    Results: In 200 of 252 (79.4\%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6\%; 227 mixed, 18.5\%; 12 noncalcified, 1\%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6\% of the patients had no plaques at all; 31.7\% had plaques in the left coronary artery tree; 46.4\% had plaques both in left and right coronary arteries, whereas 1.2\% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P {\textless} 0.001) than the weighted random approach by revealing 48 initially missed plaques.
    Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.},
    number = {8},
    journal = {Investigative Radiology},
    author = {Saur, Stefan C. and Cattin, Philippe C. and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Alkadhi, Hatem},
    year = {2009},
    pages = {483--490}
}
Download Endnote/RIS citation
TY - JOUR
TI - Prediction Rules for the Detection of Coronary Artery Plaques: Evidence from Cardiac CT
AU - Saur, Stefan C.
AU - Cattin, Philippe C.
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Alkadhi, Hatem
T2 - Investigative Radiology
AB - Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques.
Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining.
Results: In 200 of 252 (79.4%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6%; 227 mixed, 18.5%; 12 noncalcified, 1%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6% of the patients had no plaques at all; 31.7% had plaques in the left coronary artery tree; 46.4% had plaques both in left and right coronary arteries, whereas 1.2% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P < 0.001) than the weighted random approach by revealing 48 initially missed plaques.
Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.
DA - 2009///
PY - 2009
DO - http://dx.doi.org/10.1097/RLI.0b013e3181a8afc4
VL - 44
IS - 8
SP - 483
EP - 490
UR - http://journals.lww.com/investigativeradiology/Abstract/2009/08000/Prediction_Rules_for_the_Detection_of_Coronary.8.aspx
ER -
Objectives: To evaluate spatial plaque distribution patterns in coronary arteries based on computed tomography coronary angiography data sets and to express the learned patterns in prediction rules. An application is proposed to use these prediction rules for the detection of initially missed plaques. Material and Methods: Two hundred fifty two consecutive patients with chronic coronary artery disease underwent contrast-enhanced dual-source computed tomography coronary angiography for clinical indications. Coronary artery plaques were manually labeled on a 16-segment coronary model and their position (ie, segments and bifurcations) and composition (ie, calcified, mixed, or noncalcified) were noted. The frequent itemset mining algorithm was used to statistically search for plaque distribution patterns. The patterns were expressed as prediction rules: given plaques at certain locations as conditions, a prediction rule gave evidence—with a certain confidence value—for a plaque at another location within the coronary artery tree. Prediction rules with the highest confidence values were evaluated and described. Furthermore, to improve manual plaque detection, all prediction rules were applied on the patient data to search for segments with potentially missed plaques. These segments were then reviewed in a second, guided reading for the existence of plaques. The same number of segments was also determined by a weighted random approach to evaluate the quality of prediction resulting from frequent itemset mining. Results: In 200 of 252 (79.4%) patients, at least one coronary plaque (range, 1–22 plaques) was found. In total 1229 plaques (990 calcified, 80.6%; 227 mixed, 18.5%; 12 noncalcified, 1%) distributed, over 916 coronary segments and 507 vessels were manually labeled. Four plaque distribution patterns were identified: 20.6% of the patients had no plaques at all; 31.7% had plaques in the left coronary artery tree; 46.4% had plaques both in left and right coronary arteries, whereas 1.2% of the patients had plaques solely in the right coronary artery (RCA). General rules were found predicting plaques in the left anterior descending artery (LAD), given plaques in segments of the RCA or in the left main artery. Further general rules predicted plaques in the LAD, given plaques in the circumflex artery. In the guided review, the segment selection based on the prediction rules from frequent itemset mining performed significantly better (P < 0.001) than the weighted random approach by revealing 48 initially missed plaques. Conclusions: This study demonstrates spatial plaque distribution patterns in coronary arteries as determined with cardiac CT. Use of the frequent itemset mining algorithm yielded rules that predicted plaques at certain sites given plaques at other sites of the coronary artery tree. Use of these prediction rules improved the manual labeling of coronary plaques as initially missed plaques could be predicted with the guided review.
The Bayesian Group-Lasso for Analyzing Contingency Tables.
Sudhir Raman, Thomas J. Fuchs, Peter J. Wild, Edgar Dahl and Volker Roth.
Proceedings of the 26th Annual International Conference on Machine Learning, p. 881–888, ICML '09, ACM, New York, NY, USA, ISBN 978-1-60558-516-1, 2009
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@inproceedings{raman_bayesian_2009,
    address = {Montreal, Quebec, Canada},
    series = {{ICML} '09},
    title = {The {Bayesian} {Group}-{Lasso} for {Analyzing} {Contingency} {Tables}},
    isbn = {978-1-60558-516-1},
    url = {http://doi.acm.org/10.1145/1553374.1553487},
    doi = {10.1145/1553374.1553487},
    booktitle = {Proceedings of the 26th {Annual} {International} {Conference} on {Machine} {Learning}},
    publisher = {ACM, New York, NY, USA},
    author = {Raman, Sudhir and Fuchs, Thomas J. and Wild, Peter J. and Dahl, Edgar and Roth, Volker},
    year = {2009},
    pages = {881--888}
}
Download Endnote/RIS citation
TY - CONF
TI - The Bayesian Group-Lasso for Analyzing Contingency Tables
AU - Raman, Sudhir
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AU - Dahl, Edgar
AU - Roth, Volker
T3 - ICML '09
C1 - Montreal, Quebec, Canada
C3 - Proceedings of the 26th Annual International Conference on Machine Learning
DA - 2009///
PY - 2009
DO - 10.1145/1553374.1553487
SP - 881
EP - 888
PB - ACM, New York, NY, USA
SN - 978-1-60558-516-1
UR - http://doi.acm.org/10.1145/1553374.1553487
ER -
Prognostic Relevance of Wnt-Inhibitory Factor-1 (WIF1) and Dickkopf-3 (DKK3) Promoter Methylation in Human Breast Cancer.
Jurgen Veeck, Peter Wild, Thomas J. Fuchs, Peter Schueffler, Arndt Hartmann, Ruth Knuchel and Edgar Dahl.
BMC Cancer, vol. 9, 1, p. 217, 2009
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@article{veeck_prognostic_2009,
    title = {Prognostic {Relevance} of {Wnt}-{Inhibitory} {Factor}-1 ({WIF}1) and {Dickkopf}-3 ({DKK}3) {Promoter} {Methylation} in {Human} {Breast} {Cancer}},
    volume = {9},
    issn = {1471-2407},
    url = {http://www.biomedcentral.com/1471-2407/9/217},
    doi = {10.1186/1471-2407-9-217},
    abstract = {Background
    Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.
    Methods
    WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.
    Results
    WIF1 and DKK3 promoter methylation were detected in 63.3\% (95/150) and 61.3\% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0\% (0/19) and DKK3 methylation in 5.3\% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54\% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97\% OS after 10 years (p {\textless} 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58\%, compared with 78\% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95\% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95\% CI: 1.0–6.0; p = 0.047) in breast cancer.
    Conclusion
    Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.},
    number = {1},
    journal = {BMC Cancer},
    author = {Veeck, Jurgen and Wild, Peter and Fuchs, Thomas J. and Schueffler, Peter and Hartmann, Arndt and Knuchel, Ruth and Dahl, Edgar},
    year = {2009},
    pages = {217}
}
Download Endnote/RIS citation
TY - JOUR
TI - Prognostic Relevance of Wnt-Inhibitory Factor-1 (WIF1) and Dickkopf-3 (DKK3) Promoter Methylation in Human Breast Cancer
AU - Veeck, Jurgen
AU - Wild, Peter
AU - Fuchs, Thomas J.
AU - Schueffler, Peter
AU - Hartmann, Arndt
AU - Knuchel, Ruth
AU - Dahl, Edgar
T2 - BMC Cancer
AB - Background
Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.
Methods
WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.
Results
WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer.
Conclusion
Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.
DA - 2009///
PY - 2009
DO - 10.1186/1471-2407-9-217
VL - 9
IS - 1
SP - 217
SN - 1471-2407
UR - http://www.biomedcentral.com/1471-2407/9/217
ER -
Background Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease. Methods WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses. Results WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3 -methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer. Conclusion Although the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.
Inter-Active Learning of Randomized Tree Ensembles for Object Detection.
Thomas J. Fuchs and Joachim M. Buhmann.
Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on Computer Vision, p. 1370–1377, ISBN 978-1-4244-4442-7, 2009
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@inproceedings{fuchs_inter-active_2009,
    title = {Inter-{Active} {Learning} of {Randomized} {Tree} {Ensembles} for {Object} {Detection}},
    isbn = {978-1-4244-4442-7},
    url = {http://dx.doi.org/10.1109/ICCVW.2009.5457452},
    doi = {http://dx.doi.org/10.1109/ICCVW.2009.5457452},
    booktitle = {Computer {Vision} {Workshops} ({ICCV} {Workshops}), 2009 {IEEE} 12th {International} {Conference} on {Computer} {Vision}},
    author = {Fuchs, Thomas J. and Buhmann, Joachim M.},
    year = {2009},
    pages = {1370--1377}
}
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TY - CONF
TI - Inter-Active Learning of Randomized Tree Ensembles for Object Detection
AU - Fuchs, Thomas J.
AU - Buhmann, Joachim M.
C3 - Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on Computer Vision
DA - 2009///
PY - 2009
DO - http://dx.doi.org/10.1109/ICCVW.2009.5457452
SP - 1370
EP - 1377
SN - 978-1-4244-4442-7
UR - http://dx.doi.org/10.1109/ICCVW.2009.5457452
ER -
Guided Review by Frequent Itemset Mining: Additional Evidence for Plaque Detection.
Stefan C. Saur, Hatem Alkadhi, Lotus Desbiolles, Thomas J. Fuchs, Gabor Szekely and Philippe C. Cattin.
International Journal of Computer Assisted Radiology and Surgery, vol. 4, 3, p. 263–271, 2009
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@article{saur_guided_2009,
    title = {Guided {Review} by {Frequent} {Itemset} {Mining}: {Additional} {Evidence} for {Plaque} {Detection}},
    volume = {4},
    url = {http://dx.doi.org/10.1007/s11548-009-0290-5},
    number = {3},
    journal = {International Journal of Computer Assisted Radiology and Surgery},
    author = {Saur, Stefan C. and Alkadhi, Hatem and Desbiolles, Lotus and Fuchs, Thomas J. and Szekely, Gabor and Cattin, Philippe C.},
    year = {2009},
    pages = {263--271}
}
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TY - JOUR
TI - Guided Review by Frequent Itemset Mining: Additional Evidence for Plaque Detection
AU - Saur, Stefan C.
AU - Alkadhi, Hatem
AU - Desbiolles, Lotus
AU - Fuchs, Thomas J.
AU - Szekely, Gabor
AU - Cattin, Philippe C.
T2 - International Journal of Computer Assisted Radiology and Surgery
DA - 2009///
PY - 2009
VL - 4
IS - 3
SP - 263
EP - 271
UR - http://dx.doi.org/10.1007/s11548-009-0290-5
ER -
Randomized Tree Ensembles for Object Detection in Computational Pathology.
Thomas J. Fuchs, Johannes Haybaeck, Peter J. Wild, Mathias Heikenwalder, Holger Moch, Adriano Aguzzi and Joachim M. Buhmann.
Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I, p. 367–378, ISVC '09, Springer-Verlag, Berlin, Heidelberg, ISBN 978-3-642-10330-8, 2009
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@inproceedings{fuchs_randomized_2009,
    address = {Las Vegas, Nevada},
    series = {{ISVC} '09},
    title = {Randomized {Tree} {Ensembles} for {Object} {Detection} in {Computational} {Pathology}},
    isbn = {978-3-642-10330-8},
    url = {http://dx.doi.org/10.1007/978-3-642-10331-5_35},
    doi = {http://dx.doi.org/10.1007/978-3-642-10331-5_35},
    booktitle = {Proceedings of the 5th {International} {Symposium} on {Advances} in {Visual} {Computing}: {Part} {I}},
    publisher = {Springer-Verlag, Berlin, Heidelberg},
    author = {Fuchs, Thomas J. and Haybaeck, Johannes and Wild, Peter J. and Heikenwalder, Mathias and Moch, Holger and Aguzzi, Adriano and Buhmann, Joachim M.},
    year = {2009},
    pages = {367--378}
}
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TY - CONF
TI - Randomized Tree Ensembles for Object Detection in Computational Pathology
AU - Fuchs, Thomas J.
AU - Haybaeck, Johannes
AU - Wild, Peter J.
AU - Heikenwalder, Mathias
AU - Moch, Holger
AU - Aguzzi, Adriano
AU - Buhmann, Joachim M.
T3 - ISVC '09
C1 - Las Vegas, Nevada
C3 - Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
DA - 2009///
PY - 2009
DO - http://dx.doi.org/10.1007/978-3-642-10331-5_35
SP - 367
EP - 378
PB - Springer-Verlag, Berlin, Heidelberg
SN - 978-3-642-10330-8
UR - http://dx.doi.org/10.1007/978-3-642-10331-5_35
ER -
Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue.
Xenofon E. Floros, Thomas J. Fuchs, Markus P. Rechsteiner, Giatgen Spinas, Holger Moch and Joachim M. Buhmann.
Medical Image Computing and Computer-Assisted Intervention MICCAI, p. 633-640, 2009
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@inproceedings{floros_graph-based_2009,
    title = {Graph-{Based} {Pancreatic} {Islet} {Segmentation} for {Early} {Type} 2 {Diabetes} {Mellitus} on {Histopathological} {Tissue}},
    url = {http://www.springerlink.com/index/2PG4628N77772L77.pdf},
    booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} {MICCAI}},
    author = {Floros, Xenofon E. and Fuchs, Thomas J. and Rechsteiner, Markus P. and Spinas, Giatgen and Moch, Holger and Buhmann, Joachim M.},
    year = {2009},
    pages = {633--640}
}
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TY - CONF
TI - Graph-Based Pancreatic Islet Segmentation for Early Type 2 Diabetes Mellitus on Histopathological Tissue
AU - Floros, Xenofon E.
AU - Fuchs, Thomas J.
AU - Rechsteiner, Markus P.
AU - Spinas, Giatgen
AU - Moch, Holger
AU - Buhmann, Joachim M.
C3 - Medical Image Computing and Computer-Assisted Intervention MICCAI
DA - 2009///
PY - 2009
SP - 633
EP - 640
UR - http://www.springerlink.com/index/2PG4628N77772L77.pdf
ER -
Automatic and Robust Forearm Segmentation Using Graph Cuts.
Philipp Fuernstahl, Thomas J. Fuchs, Andreas Schweizer, Ladislav Nagy, Gabor Szekely and Matthias Harders.
5th IEEE International Symposium on Biomedical Imaging. ISBI 2008, p. 77–80, IEEE, ISBN 978-1-4244-2002-5, 2008
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@inproceedings{fuernstahl_automatic_2008,
    address = {Paris, France},
    title = {Automatic and {Robust} {Forearm} {Segmentation} {Using} {Graph} {Cuts}},
    isbn = {978-1-4244-2002-5},
    url = {http://dx.doi.org/10.1109/ISBI.2008.4540936},
    doi = {10.1109/ISBI.2008.4540936},
    abstract = {The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.},
    booktitle = {5th {IEEE} {International} {Symposium} on {Biomedical} {Imaging}. {ISBI} 2008},
    publisher = {IEEE},
    author = {Fuernstahl, Philipp and Fuchs, Thomas J. and Schweizer, Andreas and Nagy, Ladislav and Szekely, Gabor and Harders, Matthias},
    year = {2008},
    pages = {77--80}
}
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TY - CONF
TI - Automatic and Robust Forearm Segmentation Using Graph Cuts
AU - Fuernstahl, Philipp
AU - Fuchs, Thomas J.
AU - Schweizer, Andreas
AU - Nagy, Ladislav
AU - Szekely, Gabor
AU - Harders, Matthias
AB - The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.
C1 - Paris, France
C3 - 5th IEEE International Symposium on Biomedical Imaging. ISBI 2008
DA - 2008///
PY - 2008
DO - 10.1109/ISBI.2008.4540936
SP - 77
EP - 80
PB - IEEE
SN - 978-1-4244-2002-5
UR - http://dx.doi.org/10.1109/ISBI.2008.4540936
ER -
The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach that is particularly suited to robustly segment joints in 3-d CT images. We incorporate knowledge about intensity, bone shape and local structures into a novel energy function. Our presented framework performs a simultaneous segmentation of both forearm bones without any user interaction.
Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Cell Carcinoma.
Thomas J. Fuchs, Tilman Lange, Peter J. Wild, Holger Moch and Joachim M. Buhmann.
Pattern Recognition. DAGM 2008, vol. 5096, p. 173–182, Lecture Notes in Computer Science, Springer-Verlag, ISBN 978-3-540-69320-8, 2008
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@inproceedings{fuchs_weakly_2008,
    address = {Berlin, Heidelberg},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Weakly {Supervised} {Cell} {Nuclei} {Detection} and {Segmentation} on {Tissue} {Microarrays} of {Renal} {Cell} {Carcinoma}},
    volume = {5096},
    isbn = {978-3-540-69320-8},
    url = {http://www.springerlink.com/index/L380G56T1V230715.pdf},
    doi = {10.1007/978-3-540-69321-},
    booktitle = {Pattern {Recognition}. {DAGM} 2008},
    publisher = {Springer-Verlag},
    author = {Fuchs, Thomas J. and Lange, Tilman and Wild, Peter J. and Moch, Holger and Buhmann, Joachim M.},
    year = {2008},
    pages = {173--182}
}
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TY - CONF
TI - Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Cell Carcinoma
AU - Fuchs, Thomas J.
AU - Lange, Tilman
AU - Wild, Peter J.
AU - Moch, Holger
AU - Buhmann, Joachim M.
T3 - Lecture Notes in Computer Science
C1 - Berlin, Heidelberg
C3 - Pattern Recognition. DAGM 2008
DA - 2008///
PY - 2008
DO - 10.1007/978-3-540-69321-
VL - 5096
SP - 173
EP - 182
PB - Springer-Verlag
SN - 978-3-540-69320-8
UR - http://www.springerlink.com/index/L380G56T1V230715.pdf
ER -
Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients.
Thomas J. Fuchs, Peter J. Wild, Holger Moch and Joachim M. Buhmann.
Proceedings of the international conference on Medical Image Computing and Computer-Assisted Intervention MICCAI, vol. 5242, p. 1-8, Lecture Notes in Computer Science, Springer-Verlag, ISBN 978-3-540-85989-5, 2008
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@inproceedings{fuchs_computational_2008,
    address = {Berlin, Heidelberg},
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Computational {Pathology} {Analysis} of {Tissue} {Microarrays} {Predicts} {Survival} of {Renal} {Clear} {Cell} {Carcinoma} {Patients}},
    volume = {5242},
    isbn = {978-3-540-85989-5},
    url = {http://dx.doi.org/10.1007/978-3-540-85990-1_1},
    doi = {10.1007/978-3-540-85990-1_1},
    booktitle = {Proceedings of the international conference on {Medical} {Image} {Computing} and {Computer}-{Assisted} {Intervention} {MICCAI}},
    publisher = {Springer-Verlag},
    author = {Fuchs, Thomas J. and Wild, Peter J. and Moch, Holger and Buhmann, Joachim M.},
    year = {2008},
    keywords = {Computer Science},
    pages = {1--8}
}
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TY - CONF
TI - Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AU - Moch, Holger
AU - Buhmann, Joachim M.
T3 - Lecture Notes in Computer Science
C1 - Berlin, Heidelberg
C3 - Proceedings of the international conference on Medical Image Computing and Computer-Assisted Intervention MICCAI
DA - 2008///
PY - 2008
DO - 10.1007/978-3-540-85990-1_1
VL - 5242
SP - 1
EP - 8
PB - Springer-Verlag
SN - 978-3-540-85989-5
UR - http://dx.doi.org/10.1007/978-3-540-85990-1_1
KW - Computer Science
ER -
Nuclear Karyopherin Alpha2 Expression Predicts Poor Survival in Patients with Advanced Breast Cancer Irrespective of Treatment Intensity.
Oleg Gluz, Peter Wild, Robert Meiler, Raihana Diallo-Danebrock, Evelyn Ting, Svjetlana Mohrmann, Gerhart Schuett, Edgar Dahl, Thomas J. Fuchs, Alexander Herr, Andreas Gaumann, Markus Frick, Christopher Poremba, Ulrike Anneliese Nitz and Arndt Hartmann.
International Journal of Cancer, vol. 123/6, IF 4,693, p. 1433–1438, 2008
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@article{gluz_nuclear_2008,
    title = {Nuclear {Karyopherin} {Alpha}2 {Expression} {Predicts} {Poor} {Survival} in {Patients} with {Advanced} {Breast} {Cancer} {Irrespective} of {Treatment} {Intensity}},
    volume = {123/6},
    issn = {1097-0215},
    url = {http://onlinelibrary.wiley.com/doi/10.1002/ijc.23628/full},
    doi = {10.1002/ijc.23628},
    number = {IF 4,693},
    journal = {International Journal of Cancer},
    author = {Gluz, Oleg and Wild, Peter and Meiler, Robert and Diallo-Danebrock, Raihana and Ting, Evelyn and Mohrmann, Svjetlana and Schuett, Gerhart and Dahl, Edgar and Fuchs, Thomas J. and Herr, Alexander and Gaumann, Andreas and Frick, Markus and Poremba, Christopher and Nitz, Ulrike Anneliese and Hartmann, Arndt},
    year = {2008},
    pages = {1433--1438}
}
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TY - JOUR
TI - Nuclear Karyopherin Alpha2 Expression Predicts Poor Survival in Patients with Advanced Breast Cancer Irrespective of Treatment Intensity
AU - Gluz, Oleg
AU - Wild, Peter
AU - Meiler, Robert
AU - Diallo-Danebrock, Raihana
AU - Ting, Evelyn
AU - Mohrmann, Svjetlana
AU - Schuett, Gerhart
AU - Dahl, Edgar
AU - Fuchs, Thomas J.
AU - Herr, Alexander
AU - Gaumann, Andreas
AU - Frick, Markus
AU - Poremba, Christopher
AU - Nitz, Ulrike Anneliese
AU - Hartmann, Arndt
T2 - International Journal of Cancer
DA - 2008///
PY - 2008
DO - 10.1002/ijc.23628
VL - 123/6
IS - IF 4,693
SP - 1433
EP - 1438
SN - 1097-0215
UR - http://onlinelibrary.wiley.com/doi/10.1002/ijc.23628/full
ER -
The Acute Coronary Syndrome – Pre-Hospital Diagnostic Quality.
Geza Gemes, Thomas J. Fuchs, Gernot Wildner, Freyja-Maria Smolle-Juettner, Josef Smolle, Kurt Stoschitzky and Gerhard Prause.
Resuscitation, vol. 66, 3, p. 323 - 330, 2005
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@article{gemes_acute_2005,
    title = {The {Acute} {Coronary} {Syndrome} – {Pre}-{Hospital} {Diagnostic} {Quality}},
    volume = {66},
    issn = {0300-9572},
    url = {http://www.sciencedirect.com/science/article/B6T19-4GV9846-3/2/bcd3755266ba68b84536b39803426401},
    doi = {DOI: 10.1016/j.resuscitation.2005.04.006},
    abstract = {Background and objective: In the Austrian emergency medical service (EMS), emergency medical technician-staffed and physician-staffed vehicles are in operation. Patients with suspected acute coronary syndromes (ACS) are treated in the pre-hospital phase and transported to the hospital by an emergency physician (EP). This study evaluates the diagnostic performance of EPs in ACS and the impact of this emergency system on the outcome of ACS in an urban area.
    Design: Retrospective case control study.
    Methods: All protocol sheets from the emergency physicians were searched for the diagnosis of ACS. The database of the emergency department (ED) was searched for patients with ACS as an admission diagnosis or ACS as discharge diagnosis. For patients admitted to an intensive care unit (ICU), the medical history from the ICU was reviewed. According to the diagnosis and the aggressiveness of therapy, patients were divided in five categories of severity at each stage of care (pre-hospital category, ED category, ICU category).
    Results: A total of 3585 patients was analysed. Only 17.8\% of the patients with ACS as the admission diagnosis and 20.3\% of the patients with ACS as the discharge diagnosis were transported by an EP. 46.8\% of the ACS diagnosis by EPs were confirmed in hospital. Patients transported by EPs showed a higher all-cause mortality in hospital (1.6\% vs. 0.6\%; p = 0.011). There was no significant correlation between the pre-hospital category of patients treated by EPs and the ED category. When a 12-lead-electrocardiogramm was recorded, the correlation improved slightly (rho: 0.139; p = 0.006).
    Conclusions: The percentage of ACS patients transported to hospital by an EP is very low, and EPs seem to be “over-aware” in the diagnosis of ACS.},
    number = {3},
    journal = {Resuscitation},
    author = {Gemes, Geza and Fuchs, Thomas J. and Wildner, Gernot and Smolle-Juettner, Freyja-Maria and Smolle, Josef and Stoschitzky, Kurt and Prause, Gerhard},
    year = {2005},
    pages = {323 -- 330}
}
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TY - JOUR
TI - The Acute Coronary Syndrome – Pre-Hospital Diagnostic Quality
AU - Gemes, Geza
AU - Fuchs, Thomas J.
AU - Wildner, Gernot
AU - Smolle-Juettner, Freyja-Maria
AU - Smolle, Josef
AU - Stoschitzky, Kurt
AU - Prause, Gerhard
T2 - Resuscitation
AB - Background and objective: In the Austrian emergency medical service (EMS), emergency medical technician-staffed and physician-staffed vehicles are in operation. Patients with suspected acute coronary syndromes (ACS) are treated in the pre-hospital phase and transported to the hospital by an emergency physician (EP). This study evaluates the diagnostic performance of EPs in ACS and the impact of this emergency system on the outcome of ACS in an urban area.
Design: Retrospective case control study.
Methods: All protocol sheets from the emergency physicians were searched for the diagnosis of ACS. The database of the emergency department (ED) was searched for patients with ACS as an admission diagnosis or ACS as discharge diagnosis. For patients admitted to an intensive care unit (ICU), the medical history from the ICU was reviewed. According to the diagnosis and the aggressiveness of therapy, patients were divided in five categories of severity at each stage of care (pre-hospital category, ED category, ICU category).
Results: A total of 3585 patients was analysed. Only 17.8% of the patients with ACS as the admission diagnosis and 20.3% of the patients with ACS as the discharge diagnosis were transported by an EP. 46.8% of the ACS diagnosis by EPs were confirmed in hospital. Patients transported by EPs showed a higher all-cause mortality in hospital (1.6% vs. 0.6%; p = 0.011). There was no significant correlation between the pre-hospital category of patients treated by EPs and the ED category. When a 12-lead-electrocardiogramm was recorded, the correlation improved slightly (rho: 0.139; p = 0.006).
Conclusions: The percentage of ACS patients transported to hospital by an EP is very low, and EPs seem to be “over-aware” in the diagnosis of ACS.
DA - 2005///
PY - 2005
DO - DOI: 10.1016/j.resuscitation.2005.04.006
VL - 66
IS - 3
SP - 323
EP - 330
SN - 0300-9572
UR - http://www.sciencedirect.com/science/article/B6T19-4GV9846-3/2/bcd3755266ba68b84536b39803426401
ER -
Background and objective: In the Austrian emergency medical service (EMS), emergency medical technician-staffed and physician-staffed vehicles are in operation. Patients with suspected acute coronary syndromes (ACS) are treated in the pre-hospital phase and transported to the hospital by an emergency physician (EP). This study evaluates the diagnostic performance of EPs in ACS and the impact of this emergency system on the outcome of ACS in an urban area. Design: Retrospective case control study. Methods: All protocol sheets from the emergency physicians were searched for the diagnosis of ACS. The database of the emergency department (ED) was searched for patients with ACS as an admission diagnosis or ACS as discharge diagnosis. For patients admitted to an intensive care unit (ICU), the medical history from the ICU was reviewed. According to the diagnosis and the aggressiveness of therapy, patients were divided in five categories of severity at each stage of care (pre-hospital category, ED category, ICU category). Results: A total of 3585 patients was analysed. Only 17.8% of the patients with ACS as the admission diagnosis and 20.3% of the patients with ACS as the discharge diagnosis were transported by an EP. 46.8% of the ACS diagnosis by EPs were confirmed in hospital. Patients transported by EPs showed a higher all-cause mortality in hospital (1.6% vs. 0.6%; p = 0.011). There was no significant correlation between the pre-hospital category of patients treated by EPs and the ED category. When a 12-lead-electrocardiogramm was recorded, the correlation improved slightly (rho: 0.139; p = 0.006). Conclusions: The percentage of ACS patients transported to hospital by an EP is very low, and EPs seem to be “over-aware” in the diagnosis of ACS.