Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review - Top-Institutions - MDSpire

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

  • By

  • Keyur Radiya

  • Henrik Lykke Joakimsen

  • Karl Øyvind Mikalsen

  • Eirik Kjus Aahlin

  • Rolv-Ole Lindsetmo

  • Kim Erlend Mortensen

  • May 12, 2023

  • 0 min

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Top Institutions in Radiology

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Hepatology

  • #2

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Artificial Intelligence

  • #3

    Johns Hopkins Hospital
    Johns Hopkins Hospital

    Baltimore, MD

    Key Differentiators

    • Radiology
    • Biomedical Engineering
    • Hepatology

  • #4

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

    San Francisco, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Hepatology

  • #5

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Radiology
    • Hepatology
    • Biomedical Informatics

Original Source(s)

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

  • by Keyur Radiya, Henrik Lykke Joakimsen, Karl Øyvind Mikalsen, Eirik Kjus Aahlin, Rolv-Ole Lindsetmo, Kim Erlend Mortensen

  • May 12, 2023

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