CoreFormer high fidelity pulmonary nodule segmentation with structural core priors and geodesic implicit fields - Top-Institutions - MDSpire

CoreFormer high fidelity pulmonary nodule segmentation with structural core priors and geodesic implicit fields

  • By

  • Yong Xi

  • Chuan Xu

  • Fan Ye

  • Min Yuan

  • Chunlin Ye

  • Lei Jiang

  • Yunhe Huang

  • Jingtao Zhang

  • Mengjie Liu

  • Xiaoming Liu

  • Bentong Yu

  • December 12, 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
    • Medical Imaging
    • Oncology
    • Biomedical Engineering

  • #2

    Stanford University
    Stanford University

    Stanford, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Computer Science

  • #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 Engineering
    • Computer Science

  • #5

    National Institutes of Health (NIH) - National Cancer Institute (NCI)
    National Institutes of Health (NIH) - National Cancer Institute (NCI)

    Bethesda, MD

    Key Differentiators

    • Cancer Research
    • Medical Imaging
    • Bioinformatics

Original Source(s)

CoreFormer high fidelity pulmonary nodule segmentation with structural core priors and geodesic implicit fields

  • by Yong Xi, Chuan Xu, Fan Ye, Min Yuan, Chunlin Ye, Lei Jiang, Yunhe Huang, Jingtao Zhang, Mengjie Liu, Xiaoming Liu, Bentong Yu

  • December 12, 2025

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