Self-supervised learning for classifying paranasal anomalies in the maxillary sinus - Top-Institutions - MDSpire

Self-supervised learning for classifying paranasal anomalies in the maxillary sinus

  • By

  • Debayan Bhattacharya

  • Finn Behrendt

  • Benjamin Tobias Becker

  • Lennart Maack

  • Dirk Beyersdorff

  • Elina Petersen

  • Marvin Petersen

  • Bastian Cheng

  • Dennis Eggert

  • Christian Betz

  • Anna Sophie Hoffmann

  • Alexander Schlaefer

  • June 8, 2024

  • 0 min

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Top Institutions in Surgery

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Otolaryngology
    • Medical AI Research

  • #2

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Otolaryngology
    • Radiology
    • Biomedical Engineering

  • #3

    Stanford University
    Stanford University

    Stanford, CA

    Key Differentiators

    • Radiology
    • Otolaryngology
    • Artificial Intelligence in Medicine

  • #4

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Radiology
    • Otolaryngology
    • Clinical AI Applications

  • #5

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

    San Francisco, CA

    Key Differentiators

    • Radiology
    • Otolaryngology
    • Medical Imaging AI

Original Source(s)

Self-supervised learning for classifying paranasal anomalies in the maxillary sinus

  • by Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Lennart Maack, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer

  • June 8, 2024

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