Dr. David Joon Ho

Dr. David Joon Ho

Dr. David Joon Ho

Memorial Sloan Kettering Cancer Center
417 East 68th Street, office Z-686
New York, NY 10065
hod@mskcc.org
+1-646-888-3911

Machine Learning Scientist

Hello. I am Machine Learning Scientist at the Thomas Fuchs Lab. I joined the Lab in January 2019. Before that, I received my PhD in Electrical and Computer Engineering at Purdue University. My research interests include digital and computational pathology, computer vision, and machine learning/artificial intelligence. More specifically, I focus on multi-class tissue segmentation of hematoxylin and eosin (H&E)-stained histopathology whole slide images from various cancer types to objectively assess and predict treatment response, mutation, and survival outcome from the images.

Publications
  • David Joon Ho, Dig V. K. Yarlagadda, Timothy M. D'Alfonso, Matthew G. Hanna, Anne Grabenstetter, Peter Ntiamoah, Edi Brogi, Lee K. Tan, Thomas J. Fuchs, "Deep Multi-Magnification Networks for Multi-Class Breast Cancer Image Segmentation," Computerized Medical Imaging and Graphics, vol. 88, March 2021. [paper] [preprint] [code]
  • David Joon Ho, Narasimhan P. Agaram, Peter J. Schueffler, Chad M. Vanderbilt, Marc-Henri Jean, Meera R. Hameed, Thomas J. Fuchs, "Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment", Proceedings of the Medical Image Computing and Computer Assisted Intervention, October 2020. [paper] [preprint]
  • Timothy M. D’Alfonso, David Joon Ho, Matthew G. Hanna, Anne Grabenstetter, Dig Vijay Kumar Yarlagadda, Luke Geneslaw, Peter Ntiamoah, Thomas J. Fuchs, Lee K. Tan, "Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens," Modern Pathology, vol. 34, August 2021. [paper]
Abstracts
  • Timothy D'Alfonso, David Joon Ho, Matthew Hanna, Anne Grabenstetter, Dig Vijay Kumar Yarlagadda, Luke Geneslaw, Peter Ntiamoah, Lee Tan, "Machine Learning as an Ancillary Tool in the Assessment of Shaved Margins for Breast Carcinoma Excision Specimens," United States and Canadian Academy of Pathology, March 2020. [abstract]
  • David Joon Ho, Akimasa Hayashi, Shigeaki Umeda, Chad M. Vanderbilt, Christine A. Iacobuzio-Donahue, Thomas J. Fuchs, "Cross cancer deep interactive learning with reduced manual training annotation for pancreatic tumor segmentation," Pathology Visions, October 2020. [poster]
  • David Joon Ho, "Microsatellite Instability Prediction by High-Confident Patches in Colorectal Cancer Whole Slide Images," Pathology AI Platform (PAIP2020), AI Pathology Challenge Workshop at Korean Society of Medical and Biological Engineering Conference, November 2020. Ranked the 10th place at PAIP2020 Challenge.
  • David Joon Ho, Timothy M. D'Alfonso, Lee K. Tan, Narasimhan P. Agaram, Meera R. Hameed, Jason C. Chang, Chad M. Vanderbilt, William Travis, Thomas J. Fuchs, "Deep learning-based whole slide image segmentation for efficient and reproducible assistance in pathology," Pathology Visions, October 2021. Oral presentation at Education & Research Track.
  • David Joon Ho, Narasimhan P. Agaram, Marc-Henri Jean, Chad M. Vanderbilt, John Healey, Paul Meyers, Thomas J. Fuchs, Meera R. Hameed, "Osteosarcoma Patient Stratification Based on Objective and Reproducible Post-Therapy Necrosis Assessment by Pixel-wise Deep Segmentation," United States and Canadian Academy of Pathology, March 2022.
Last Updated: 11/22/2021