Bridging radiology and pathology: domain-generalized cross-modal learning for clinical applications - Top-Institutions - MDSpire

Bridging radiology and pathology: domain-generalized cross-modal learning for clinical applications

  • By

  • Xiang Zhong

  • Zhuo Gu

  • Manimurugan Shanmuganathan

  • Meng Li

  • Hao Sun

  • Mingming Du

  • Qian Chen

  • Guoqin Jiang

  • February 16, 2026

  • 0 min

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Top Institutions in Pathology

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Pathology
    • Biomedical Informatics
    • Oncology

  • #2

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Radiology
    • Pathology
    • Biomedical Data Science
    • Cancer Biology

  • #3

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Radiology
    • Pathology
    • Biomedical Engineering
    • Oncology

  • #4

    University of California, San Francisco (UCSF)
    University of California, San Francisco (UCSF)

    San Francisco, CA

    Key Differentiators

    • Radiology
    • Pathology
    • Biomedical Informatics
    • Cancer Research

  • #5

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Oncology
    • Radiology
    • Pathology
    • Computational Biology

Original Source(s)

Bridging radiology and pathology: domain-generalized cross-modal learning for clinical applications

  • by Xiang Zhong, Zhuo Gu, Manimurugan Shanmuganathan, Meng Li, Hao Sun, Mingming Du, Qian Chen, Guoqin Jiang

  • February 16, 2026

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