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David Joon Ho

Formats
  1. Journal Article (6)
  2. Meeting Abstract (4)
  3. Conference Paper (1)
  4. Correspondence (1)
Groups
  1. Department of Pathology and Laboratory Medicine (10)
  2. Center for Pancreatic Cancer Research (5)
  3. Medical Physics (4)
  4. Surgery (4)
  5. Orthopaedic Service (3)
  6. Pediatrics, Department of (3)

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People
  1. Chad Vanderbilt (5)
  2. Meera Hameed (5)
  3. Narasimhan Agaram (5)
  4. Marc-Henri Jean (4)
  5. Thomas Fuchs (4)
  6. Timothy D'Alfonso (4)

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Years
  1. 2024 (1)
  2. 2023 (3)
  3. 2022 (3)
  4. 2021 (2)
  5. 2020 (3)
Publications
  1. Modern Pathology (3)
  2. Laboratory Investigation (2)
  3. American Journal of Pathology (1)
  4. Cancer Research (1)
  5. Computerized Medical Imaging and Graphics (1)
  6. Journal of Medical Imaging (1)

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Publishers
  1. Nature Research (3)
  2. Elsevier Science, Inc. (2)
  3. Nature Publishing Group (2)
  4. American Association for Cancer Research (1)
  5. Elsevier Inc. (1)
  6. SPIE (1)

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Results (12)

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Citation style sort is year ascending, and then by author or title. Author ascending is default.
  • Toward deploying a deep learning model for diagnosis of rhabdomyosarcoma
    Correspondence

    Ho, D. J.; Agaram, N. P.; Frankel, A. O.; Lathara, M.; Catchpoole, D.; Keller, C.; Hameed, M. R.
    Modern Pathology (2024)

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  • PAIP 2020: Microsatellite instability prediction in colorectal cancer
    Journal Article

    Kim, K.; Lee, K.; Cho, S.; Kang, D. U.; Park, S.; Kang, Y.; Kim, H.; Choe, G.; Moon, K. C.; Lee, K. S.; more...
    Medical Image Analysis (2023)

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  • Deep learning–based objective and reproducible osteosarcoma chemotherapy response assessment and outcome prediction
    Journal Article

    Ho, D. J.; Agaram, N. P.; Jean, M. H.; Suser, S. D.; Chu, C.; Vanderbilt, C. M.; Meyers, P. A.; Wexler, L. H.; Healey, J. H.; Fuchs, T. J.; more...
    American Journal of Pathology (2023)

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  • Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation
    Journal Article

    Ho, D. J.; Chui, M. H.; Vanderbilt, C. M.; Jung, J.; Robson, M. E.; Park, C. S.; Roh, J.; Fuchs, T. J.
    Journal of Pathology Informatics (2023)

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  • Prediction of neoadjuvant treatment outcomes with multimodal data integration in breast cancer
    Meeting Abstract

    Khosravi, P.; Sutton, E. J.; Jee, J.; Dalfonso, T.; Fong, C. J.; Rose, D.; Da Silva, E. M.; Kohli, A.; Ho, D. J.; Ahmed, M. S.; more...
    Cancer Research (2022)

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  • Osteosarcoma patient stratification based on objective and reproducible post-therapy necrosis assessment by pixel-wise deep segmentation
    Meeting Abstract

    Ho, D.; Agaram, N.; Jean, M. H.; Vanderbilt, C.; Healey, J.; Meyers, P.; Fuchs, T.; Hameed, M.
    Modern Pathology (2022)

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  • Osteosarcoma patient stratification based on objective and reproducible post-therapy necrosis assessment by pixel-wise deep segmentation
    Meeting Abstract

    Ho, D.; Agaram, N.; Jean, M. H.; Vanderbilt, C.; Healey, J.; Meyers, P.; Fuchs, T.; Hameed, M.
    Laboratory Investigation (2022)

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  • Multi-magnification-based machine learning as an ancillary tool for the pathologic assessment of shaved margins for breast carcinoma lumpectomy specimens
    Journal Article

    D'Alfonso, T. M.; Ho, D. J.; Hanna, M. G.; Grabenstetter, A.; Yarlagadda, D. V. K.; Geneslaw, L.; Ntiamoah, P.; Fuchs, T. J.; Tan, L. K.
    Modern Pathology (2021)

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  • Deep Multi-Magnification Networks for multi-class breast cancer image segmentation
    Journal Article

    Ho, D. J.; Yarlagadda, D. V. K.; D'Alfonso, T. M.; Hanna, M. G.; Grabenstetter, A.; Ntiamoah, P.; Brogi, E.; Tan, L. K.; Fuchs, T. J.
    Computerized Medical Imaging and Graphics (2021)

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  • Deep interactive learning: An efficient labeling approach for deep learning-based osteosarcoma treatment response assessment
    Conference Paper

    Ho, D. J.; Agaram, N. P.; Schüffler, P. J.; Vanderbilt, C. M.; Jean, M. H.; Hameed, M. R.; Fuchs, T. J.
    Springer (2020)

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