Publications

Journal Papers

[J10] G.Y. Kim*, Y. Jeon*, H. Jeong*, S. Lee, H. Kim, B. Lee, K.-H. Jung#, D.J. Ho#, Y.-L. Choi#, “MuCoSA: Multi-contextual similarity assessment for histopathology image search,” Journal of Pathology Informatics, Vol. 20, 100533, January 2026.

[J9] D.J. Ho*, J.C. Chang*, R.G. Aly, H.C.T. Nguyen, P.S. Adusumilli, T.J. Fuchs, W.D. Travis#, C.M. Vanderbilt#, “Deep Learning-Based Segmentation of Lung Adenocarcinoma Whole-Slide Images for Objective Grading, Tumor Spread Through Air Spaces Identification, and Mutation Prediction,” Modern Pathology, Vol. 38, No. 12, 100907, December 2025.

[J8] P. Khosravi, T.J. Fuchs, D.J. Ho, “Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality,” Cancer Research, Vol. 85, No. 13, pp. 2356-2367, July 2025.

[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

[C9] H.C.T. Nguyen and D.J. Ho, “fmMAP: A Framework Reducing Site-Bias Batch Effect from Foundation Models in Pathology,” Proceedings of the Computational Pathology and Multimodal Data Workshop at the International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2025, Daejeon, Republic of Korea.

[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.

Others

[O2] D.J. Ho, N.P. Agaram, J.H. Healey, and M.R. Hameed, “A Need for Multi-Institutional Collaboration for Deep Learning-Driven Assessment of Osteosarcoma Treatment Response,” The American Journal of Pathology, Vol. 195, No. 6, pp. 1036-1039, June 2025.

[O1] D.J. Ho*, N.P. Agaram*, A.O. Frankel, M. Lathara, D. Catchpoole, C. Keller, and M.R. Hameed, “Toward Deploying a Deep Learning Model for Diagnosis of Rhabdomyosarcoma,” Modern Pathology, Vol. 37, No. 3, 100421, March 2024.

Patents

[P3] D.J. Ho, C. Vanderbilt, N.P. Agaram, T.J. Fuchs, M.R. Hameed, “Determining predicted outcomes of subjects with cancer based on segmentations of biomedical images,” US Patent Application 18/881,208.

[P2] T. Fuchs and D.J. Ho, “Deep interactive learning for image segmentation models,” US Patent Number 11,176,677.

[P1] T. Fuchs and D.J. Ho, “Deep multi-magnification networks for multi-class image segmentation,” US Patent Number 11,501,434.