GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation - Top-Institutions - MDSpire

GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation

  • By

  • Ruijie Ming

  • Fengpin Wang

  • Taotao Zheng

  • Zhongjian Yu

  • Xiaoping Huang

  • Shuangyan Huang

  • Han Tian

  • Wei Wang

  • Jinhai Deng

  • Huawen Liu

  • Yanfang Zheng

  • December 30, 2025

  • 0 min

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Top Institutions in General Medicine

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Oncology
    • Biomedical Engineering

  • #2

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Radiology
    • Computer Science
    • Oncology

  • #3

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Radiology
    • Biomedical Engineering
    • Oncology

  • #4

    University of California, Los Angeles (UCLA)
    University of California, Los Angeles (UCLA)

    Los Angeles, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Oncology

  • #5

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Radiology
    • Oncology
    • Biomedical Engineering

Original Source(s)

GLANCE: continuous global-local exchange with consensus fusion for robust nodule segmentation

  • by Ruijie Ming, Fengpin Wang, Taotao Zheng, Zhongjian Yu, Xiaoping Huang, Shuangyan Huang, Han Tian, Wei Wang, Jinhai Deng, Huawen Liu, Yanfang Zheng

  • December 30, 2025

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