Random features meet MIL: a deep GP approach to colorectal MSI prediction - Top-Institutions - MDSpire

Random features meet MIL: a deep GP approach to colorectal MSI prediction

  • By

  • Shixuan Shen

  • Zeyang Wang

  • Tianmu Liu

  • Kangle Ma

  • Zhen Tian

  • Fuqiang Zhang

  • Qingyue Zhang

  • December 15, 2025

  • 0 min

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Top Institutions in General Medicine

Brief introduction explaining scope and methodology.

  • #1

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Oncology
    • Computational Pathology
    • Machine Learning

  • #2

    Dana-Farber Cancer Institute
    Dana-Farber Cancer Institute

    Boston, MA

    Key Differentiators

    • Oncology
    • Cancer Genomics
    • Artificial Intelligence

  • #3

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Biomedical Informatics
    • Oncology
    • Machine Learning

  • #4

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Oncology
    • Pathology
    • Computational Medicine

  • #5

    Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)
    Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

    Cambridge, MA

    Key Differentiators

    • Artificial Intelligence
    • Biomedical Engineering
    • Computational Biology

Original Source(s)

Random features meet MIL: a deep GP approach to colorectal MSI prediction

  • by Shixuan Shen, Zeyang Wang, Tianmu Liu, Kangle Ma, Zhen Tian, Fuqiang Zhang, Qingyue Zhang

  • December 15, 2025

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