StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT - Top-Institutions - MDSpire

StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT

  • By

  • Mengjie Liu

  • Yuxin Yao

  • Jinyong Jia

  • Jiali Yao

  • Zhengze Huang

  • Ziyang Zeng

  • Guangjin Pu

  • Yan Wu

  • Yuqi Bai

  • Bin Wang

  • Lili Jiang

  • February 3, 2026

  • 0 min

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Top Institutions in Oncology

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Oncology
    • Medical Imaging AI

  • #2

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Oncology
    • Radiology
    • Computational Medicine

  • #3

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Oncology

  • #4

    Johns Hopkins Hospital
    Johns Hopkins Hospital

    Baltimore, MD

    Key Differentiators

    • Radiology
    • Oncology
    • Biomedical Engineering

  • #5

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

    Los Angeles, CA

    Key Differentiators

    • Radiology
    • Oncology
    • Artificial Intelligence

Original Source(s)

StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT

  • by Mengjie Liu, Yuxin Yao, Jinyong Jia, Jiali Yao, Zhengze Huang, Ziyang Zeng, Guangjin Pu, Yan Wu, Yuqi Bai, Bin Wang, Lili Jiang

  • February 3, 2026

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