Computational Pathology

Computational Pathology comprises automated cell and cell nucleus detection, segmentation and staining estimation. Modern random forest models are capable to learn from expert annotated images the differences between various kinds of cancer related outcomes.

We collaborate with outstanding institutions in several projects to learn how pathologists understand the images and which apects are important for reliable, repoducible and more objective diagnostics.

Current Projects

Mitochondria Detection

Automatic Mitochondria Quantifaction

Mitochondria play an important role for energy delivery in every living cell. In cancer, these organelles might be disturbed due to several mutation machanisms and it is unclear how these mechanism correlate with tumor type and hypoxia. We therefore investigate different mitochondria levels in diverse renal cell carcinoma subtypes.

Contact: Peter J. Schüffler

Collaboration: Judy Sarungbam, Satish K. Tickoo


Urine Cell Classification

Urine Cell Detection and Classification

The detection and classification of urine cells in cytology images is a key step in the classification of subgroups of this disorder. In contrast to histology, where the image background commonly is highly variant and shows morphological structure of the tissue, cytology enables much clearer separation of foreground and background which facilicates cell detection and classification.

Contact: Peter J. Schüffler

Collaboration: Oscar Lin, S. Joseph Sirintrapun

Review Articles
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
PDF   URL   BibTeX   Endnote / RIS   Abstract
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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.
Computational Pathology in Cancer Research and Clinical Practice
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
Download BibTeX citation
@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.
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
Download BibTeX citation
@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
PDF    URL   BibTeX   Endnote / RIS   Abstract
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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 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
PDF   URL   BibTeX   Endnote / RIS   Abstract
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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.
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
PDF    URL   BibTeX   Endnote / RIS   Abstract
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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 -
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}
}
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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 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.
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.
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 -
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 -
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 -
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 -