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David Joon Ho, Ph.D

Name
Ho, David Joon
Faculty Appointment:
(title, department)
Assistant Professor, Department of Cancer AI and Digital Health
Area of Expertise
Artificial Intelligence, Deep Learning, Computer Vision, Biomedical Image Analysis
Contact no
2755
E-mail
hod@ncc.re.kr
Work Experience
2022-2023 Instructor, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center
2019-2022 Machine Learning Scientist, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center
2018 Graduate Lecturer, Department of Electrical and Computer Engineering, Purdue University
2017 Summer Intern, HP Labs
Educational Background
2019 Ph.D. Electrical and Computer Engineering, Purdue University
2012 M.S. Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
2010 B.S. Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Research Interests
Dr. David Joon Ho has led multiple AI research projects on biomedical image analysis to support clinicians and improve patient care.
His research interests include digital and computational pathology, computer vision, and deep learning.
International Collaboration
Member, Digital Pathology Association
Member, Korean-American Scientists and Engineers Association
Achievements
Journal Papers

[J7]K. Kim, K. Lee, S. Cho, D.U. Kang, S. Park, Y. Kang, H. Kim, G. Choe, K.C. Moon, K.S. Lee, J.H. Park, C. Hong, R. Nateghi, F. Pourakpour, X. Wang, S. Yang, S.A.F. Jahromi, A. Khani, H.-R. Kim, D.-H. Choi, C.H. Han, J.T. Kwak, F. Zhang, B. Han, D.J. Ho, G.H. Kang, S.Y. Chun, W.-K. Jeong, P. Park, J. Choi, "PAIP 2020: Microsatellite instability prediction in colorectal cancer," Medical Image Analysis, Vol. 89, 102886, October 2023.

[J6]D.J. Ho*, N.P. Agaram*, M.-H. Jean, S.D. Suser, C. Chu, C.M. Vanderbilt, P.A. Meyers, L.H. Wexler, J.H. Healey, T.J. Fuchs, M.R. Hameed, "Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction," The American Journal of Pathology, Vol. 193, No. 3, pp. 341-349, March 2023.

[J5]D.J. Ho, M.H. Chui, C.M. Vanderbilt, J. Jung, M.E. Robson, C.-S. Park, J. Roh, T.J. Fuchs, "Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation," Journal of Pathology Informatics, Vol. 14, 100160, 2023.

[J4]T.M. D'Alfonso, D.J. Ho, M.G. Hanna, A. Grabenstetter, D.V.K. Yarlagadda, L. Geneslaw, P. Ntiamoah, T.J. Fuchs, and L.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, pp. 1487–1494, August 2021.

[J3]D.J. Ho, D.V.K. Yarlagadda, T.M. D'Alfonso, M.G. Hanna, A. Grabenstetter, P. Ntiamoah, E. Brogi, L.K. Tan, and T.J. Fuchs, "Deep Multi-Magnification Networks for multi-class breast cancer image segmentation," Computerized Medical Imaging and Graphics, Vol. 88, 101866, March 2021.

[J2]D.J. Ho, D. Mas Montserrat, C. Fu, P. Salama, K.W. Dunn, and E.J. Delp, "Sphere estimation network: three-dimensional nuclei detection of fluorescence microscopy images," Journal of Medical Imaging, Vol. 7, No. 4, 044003, August 2020.

[J1]K.W. Dunn, C. Fu, D.J. Ho, S. Lee, S. Han, P. Salama, and E.J. Delp, "DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data," Scientific Reports, Vol. 9, 18295, December 2019.


Conference Papers

[C8]D.J. Ho*, N.P. Agaram*, P.J. Schueffler, C.M. Vanderbilt, M.-H. Jean, M.R. Hameed, and T.J. Fuchs, "Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment," Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 540-549, October 2020, Virtual.

[C7]D.J. Ho, S. Han, C. Fu, P. Salama, K.W. Dunn, and E.J. Delp, "Center-extraction-based three dimensional nuclei instance segmentation of fluorescence microscopy images," Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, pp. 1-4, May 2019, Chicago, IL.

[C6]C. Fu, S. Lee, D.J. Ho, S. Han, P. Salama, K.W. Dunn, and E.J. Delp, "Three dimensional fluorescence microscopy image synthesis and segmentation," Proceedings of the Computer Vision for Microscopy Image Analysis workshop at Computer Vision and Pattern Recognition, pp. 2334-2342, June 2018, Salt Lake City, UT.

[C5]D.J. Ho, C. Fu, P. Salama, K.W. Dunn, and E.J. Delp, "Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks," Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 418-422, April 2018, Washington D.C.

[C4]D.J. Ho and Q. Lin, "Person segmentation using convolutional neural networks with dilated convolutions," Proceedings of the IS&T International Symposium on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World, pp 455-1-455-7, January 2018, Burlingame, CA.

[C3]D.J. Ho, C. Fu, P. Salama, K.W. Dunn, and E.J. Delp, "Nuclei segmentation of fluorescence microscopy images using three dimensional convolutional neural networks," Proceedings of the Computer Vision for Microscopy Image Analysis workshop at Computer Vision and Pattern Recognition, pp. 834-842, July 2017, Honolulu, HI.

[C2]C. Fu, D.J. Ho, S. Han, P. Salama, K.W. Dunn, and E.J. Delp, "Nuclei segmentation of fluorescence microscopy images using convolutional neural networks," Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 704-708, April 2017, Melbourne, Australia.

[C1] D.J. Ho, P. Salama, K.W. Dunn, and E.J. Delp, "Boundary segmentation for fluorescence microscopy using steerable filters," Proceedings of the SPIE Medical Imaging: Image Processing, 101330E, February 2017, Orlando, FL.

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