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 a 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 computational pathology, computer vision, and machine learning. More specifically, I work on multi-class tissue segmentation of histopathology images from various cancer types and pursue further analyses such as treatment response assessment, mutation prediction, and treatment response prediction.

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]
  • 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]
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.
Last Updated: 1/21/2021