Closed loop text guided framework for lung cancer lesion segmentation and quantification - Top-Institutions - MDSpire

Closed loop text guided framework for lung cancer lesion segmentation and quantification

  • By

  • Shiyang Wang

  • Ziyi Wang

  • Wanfu Men

  • Zhenyu Song

  • Dayu Hu

  • Tianyu Liu

  • Boyang Wang

  • Dexing Kong

  • Xuehao Li

  • Kaiming Ren

  • Mingrui Shao

  • February 12, 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 Informatics

  • #2

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Artificial Intelligence

  • #3

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Radiology
    • Computer Science
    • Oncology

  • #4

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

    Los Angeles, CA

    Key Differentiators

    • Radiology
    • Biomedical Engineering
    • Artificial Intelligence

  • #5

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Oncology
    • Radiology
    • Medical Imaging Informatics

Original Source(s)

Closed loop text guided framework for lung cancer lesion segmentation and quantification

  • by Shiyang Wang, Ziyi Wang, Wanfu Men, Zhenyu Song, Dayu Hu, Tianyu Liu, Boyang Wang, Dexing Kong, Xuehao Li, Kaiming Ren, Mingrui Shao

  • February 12, 2026

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