What are you looking at? Modality contribution in multimodal medical deep learning - Top-Institutions - MDSpire

What are you looking at? Modality contribution in multimodal medical deep learning

  • By

  • Christian Gapp

  • Elias Tappeiner

  • Martin Welk

  • Karl Fritscher

  • Elke R. Gizewski

  • Rainer Schubert

  • October 2, 2025

  • 0 min

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Top Institutions in Surgery

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)
    Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

    Cambridge, MA

    Key Differentiators

    • Biomedical Informatics
    • Artificial Intelligence
    • Medical Imaging

  • #2

    Stanford University - Center for Artificial Intelligence in Medicine & Imaging (AIMI)
    Stanford University - Center for Artificial Intelligence in Medicine & Imaging (AIMI)

    Stanford, CA

    Key Differentiators

    • Radiology
    • Biomedical Data Science
    • Machine Learning

  • #3

    Johns Hopkins University - Malone Center for Engineering in Healthcare
    Johns Hopkins University - Malone Center for Engineering in Healthcare

    Baltimore, MD

    Key Differentiators

    • Biomedical Engineering
    • Clinical Informatics
    • AI in Medicine

  • #4

    University of California, San Francisco (UCSF) - Bakar Computational Health Sciences Institute
    University of California, San Francisco (UCSF) - Bakar Computational Health Sciences Institute

    San Francisco, CA

    Key Differentiators

    • Biomedical Data Science
    • Medical Imaging
    • Clinical AI

  • #5

    University of Toronto - Vector Institute for Artificial Intelligence
    University of Toronto - Vector Institute for Artificial Intelligence

    Toronto, ON

    Key Differentiators

    • Machine Learning
    • Medical Imaging
    • Biomedical Informatics

Original Source(s)

What are you looking at? Modality contribution in multimodal medical deep learning

  • by Christian Gapp, Elias Tappeiner, Martin Welk, Karl Fritscher, Elke R. Gizewski, Rainer Schubert

  • October 2, 2025

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