Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features - Top-Institutions - MDSpire

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features

  • By

  • Luis Carlos Rivera Monroy

  • Leonhard Rist

  • Christian Ostalecki

  • Andreas Bauer

  • Julio Vera

  • Katharina Breininger

  • Andreas Maier

  • October 7, 2024

  • 0 min

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Top Institutions in Surgery

Brief introduction explaining scope and methodology.

  • #1

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Dermatopathology
    • Computational Pathology
    • Oncology
    • Artificial Intelligence

  • #2

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Dermatology
    • Computational Imaging
    • Artificial Intelligence
    • Pathology

  • #3

    Dana-Farber Cancer Institute / Brigham and Women's Hospital
    Dana-Farber Cancer Institute / Brigham and Women's Hospital

    Boston, MA

    Key Differentiators

    • Oncology
    • Dermatopathology
    • Computational Pathology
    • AI in Medicine

  • #4

    Johns Hopkins University School of Medicine
    Johns Hopkins University School of Medicine

    Baltimore, MD

    Key Differentiators

    • Dermatology
    • Pathology
    • Computational Biology
    • Machine Learning

  • #5

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

    San Francisco, CA

    Key Differentiators

    • Dermatology
    • Pathology
    • Biomedical Imaging
    • AI in Healthcare

Original Source(s)

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features

  • by Luis Carlos Rivera Monroy, Leonhard Rist, Christian Ostalecki, Andreas Bauer, Julio Vera, Katharina Breininger, Andreas Maier

  • October 7, 2024

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