A novel contrastive learning approach utilizing pathological prior knowledge for improved classification of histological types in lung squamous cell carcinoma - Top-Institutions - MDSpire

A novel contrastive learning approach utilizing pathological prior knowledge for improved classification of histological types in lung squamous cell carcinoma

  • By

  • Mingci Huang

  • Weijin Xiao

  • Gen Lin

  • Chao Li

  • Haipeng Xu

  • Yunjian Huang

  • Shengjia Chen

  • Chuanben Chen

  • Yang Sun

  • Qiaofeng Zhong

  • December 17, 2025

  • 0 min

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Top Institutions in Pathology

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 School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Digital Pathology
    • Artificial Intelligence
    • Cancer Biology

  • #3

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Pathology
    • Biomedical Engineering
    • Machine Learning

  • #4

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

    Boston, MA

    Key Differentiators

    • Pathology
    • Computational Medicine
    • Cancer Informatics

  • #5

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

    San Francisco, CA

    Key Differentiators

    • Digital Pathology
    • Machine Learning
    • Oncology

Original Source(s)

A novel contrastive learning approach utilizing pathological prior knowledge for improved classification of histological types in lung squamous cell carcinoma

  • by Mingci Huang, Weijin Xiao, Gen Lin, Chao Li, Haipeng Xu, Yunjian Huang, Shengjia Chen, Chuanben Chen, Yang Sun, Qiaofeng Zhong

  • December 17, 2025

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