Adaptive sensitivity-fisher regularization for heterogeneous transfer learning of vascular segmentation in laparoscopic videos - Top-Institutions - MDSpire

Adaptive sensitivity-fisher regularization for heterogeneous transfer learning of vascular segmentation in laparoscopic videos

  • By

  • Xinkai Zhao

  • Yuichiro Hayashi

  • Masahiro Oda

  • Takayuki Kitasaka

  • Kazunari Misawa

  • Kensaku Mori

  • June 6, 2025

  • 0 min

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Top Institutions in Surgery

Brief introduction explaining scope and methodology.

  • #1

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Surgical Innovation
    • Biomedical Engineering
    • Computer Vision

  • #2

    Stanford University
    Stanford University

    Stanford, CA

    Key Differentiators

    • Artificial Intelligence
    • Surgical Robotics
    • Medical Imaging

  • #3

    Massachusetts Institute of Technology (MIT)
    Massachusetts Institute of Technology (MIT)

    Cambridge, MA

    Key Differentiators

    • Machine Learning
    • Robotics
    • Biomedical Engineering

  • #4

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

    San Francisco, CA

    Key Differentiators

    • Surgical Oncology
    • Medical Imaging
    • Computer-Assisted Surgery

  • #5

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Surgical Research
    • Biomedical Informatics
    • Medical Imaging

Original Source(s)

Adaptive sensitivity-fisher regularization for heterogeneous transfer learning of vascular segmentation in laparoscopic videos

  • by Xinkai Zhao, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

  • June 6, 2025

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