Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence - Top-Institutions - MDSpire

Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence

  • By

  • Maximilian Ferle

  • Jonas Ader

  • Thomas Wiemers

  • Nora Grieb

  • Beatrice Berneck

  • Adrian Lindenmeyer

  • Hartmut Goldschmidt

  • Elias K. Mai

  • Uta Bertsch

  • Hans-Jonas Meyer

  • Thomas Neumuth

  • Markus Kreuz

  • Kristin Reiche

  • Maximilian Merz

  • May 11, 2026

  • 0 min

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Top Institutions in Oncology

Brief introduction explaining scope and methodology.

  • #1

    Stanford University
    Stanford University

    Stanford, California

    Key Differentiators

    • Oncology
    • Radiation Oncology

  • #2

    University of Warwick
    University of Warwick

    Coventry, England

    Key Differentiators

    • Computer Science
    • Oncology

  • #3

    GMMG-MM5 study
    GMMG-MM5 study

    N/A, N/A

    Key Differentiators

    • Oncology

  • #4

    our own institution
    our own institution

    N/A, N/A

    Key Differentiators

    • Oncology

  • #5

    Mount Sinai Tisch Cancer Center
    Mount Sinai Tisch Cancer Center

    New York, New York

    Key Differentiators

    • Oncology

Original Source(s)

Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence

  • by Maximilian Ferle, Jonas Ader, Thomas Wiemers, Nora Grieb, Beatrice Berneck, Adrian Lindenmeyer, Hartmut Goldschmidt, Elias K. Mai, Uta Bertsch, Hans-Jonas Meyer, Thomas Neumuth, Markus Kreuz, Kristin Reiche, Maximilian Merz

  • May 11, 2026

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