Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis - Top-Institutions - MDSpire

Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis

  • By

  • Shenghan Lou

  • Genshen Mo

  • Xiao Zhang

  • Hao Wang

  • Hao Li

  • Keru Ma

  • Huiying Li

  • Xinyue Zhang

  • Meihong Yan

  • Haonan Xie

  • Yuze Huang

  • Chuangqi Li

  • Siyuan Ma

  • Hongxue Meng

  • Lei Cao

  • Peng Han

  • December 6, 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

    • Computational Pathology
    • Oncology
    • Machine Learning

  • #2

    Stanford University
    Stanford University

    Stanford, CA

    Key Differentiators

    • Biomedical Informatics
    • Pathology
    • Artificial Intelligence

  • #3

    Massachusetts General Hospital / Harvard Medical School
    Massachusetts General Hospital / Harvard Medical School

    Boston, MA

    Key Differentiators

    • Pathology
    • Cancer Research
    • Computational Medicine

  • #4

    The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center

    Houston, TX

    Key Differentiators

    • Cancer Informatics
    • Pathology
    • Machine Learning

  • #5

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Pathology
    • Biomedical Engineering
    • Artificial Intelligence

Original Source(s)

Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis

  • by Shenghan Lou, Genshen Mo, Xiao Zhang, Hao Wang, Hao Li, Keru Ma, Huiying Li, Xinyue Zhang, Meihong Yan, Haonan Xie, Yuze Huang, Chuangqi Li, Siyuan Ma, Hongxue Meng, Lei Cao, Peng Han

  • December 6, 2025

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