A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI - Top-Institutions - MDSpire

A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI

  • By

  • Roham Hadidchi

  • Anchita Agrawal

  • Michael Z. Liu

  • Takouhie Maldijan

  • Yihui Zhu

  • Hien Quang Nguyen

  • Jinyu Lu

  • Della Makower

  • Susan Fineberg

  • Tim Q. Duong

  • December 17, 2025

  • 0 min

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Top Institutions in Radiology

Brief introduction explaining scope and methodology.

  • #1

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Breast Imaging
    • Computational Pathology
    • Oncology
    • Artificial Intelligence

  • #2

    Dana-Farber Cancer Institute / Brigham and Women’s Hospital
    Dana-Farber Cancer Institute / Brigham and Women’s Hospital

    Boston, MA

    Key Differentiators

    • Breast Oncology
    • Radiology
    • Computational Medicine
    • Machine Learning

  • #3

    MD Anderson Cancer Center
    MD Anderson Cancer Center

    Houston, TX

    Key Differentiators

    • Breast Cancer Imaging
    • Radiation Oncology
    • Artificial Intelligence
    • Pathology

  • #4

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • Oncology
    • Machine Learning

  • #5

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Radiology
    • Pathology
    • Oncology
    • Artificial Intelligence

Original Source(s)

A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI

  • by Roham Hadidchi, Anchita Agrawal, Michael Z. Liu, Takouhie Maldijan, Yihui Zhu, Hien Quang Nguyen, Jinyu Lu, Della Makower, Susan Fineberg, Tim Q. Duong

  • December 17, 2025

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