Robust Framework Utilizing Deep Neural Networks for Automated Segmentation and Analysis of Skin Lesions - Top-Institutions - MDSpire

Robust Framework Utilizing Deep Neural Networks for Automated Segmentation and Analysis of Skin Lesions

  • By

  • Khlood M Mehdar

  • Toufique A Soomro

  • Ahmed Ali

  • Faisal Bin Ubaid

  • Muhammad Irfan

  • Hanan T Halawani

  • Aisha M Mashraqi

  • Sabah Elshafie Mohammed Elshafie

  • Abdullah A Asiri

  • Muawia Abdelkafi Magzoub

  • March 1, 2026

  • 0 min

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Top Institutions in Dermatology

Brief introduction explaining scope and methodology.

  • #1

    Memorial Sloan Kettering Cancer Center
    Memorial Sloan Kettering Cancer Center

    New York, NY

    Key Differentiators

    • Dermatology
    • Oncology
    • Medical Imaging
    • Artificial Intelligence

  • #2

    Stanford University School of Medicine
    Stanford University School of Medicine

    Stanford, CA

    Key Differentiators

    • Dermatology
    • Biomedical Informatics
    • Artificial Intelligence

  • #3

    Harvard Medical School / Massachusetts General Hospital
    Harvard Medical School / Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Dermatology
    • Oncology
    • Medical Imaging
    • Computational Medicine

  • #4

    Johns Hopkins University
    Johns Hopkins University

    Baltimore, MD

    Key Differentiators

    • Dermatology
    • Biomedical Engineering
    • Artificial Intelligence

  • #5

    University of California, San Francisco (UCSF)
    University of California, San Francisco (UCSF)

    San Francisco, CA

    Key Differentiators

    • Dermatology
    • Medical Imaging
    • Artificial Intelligence

Original Source(s)

Robust Framework Utilizing Deep Neural Networks for Automated Segmentation and Analysis of Skin Lesions

  • by Khlood M Mehdar, Toufique A Soomro, Ahmed Ali, Faisal Bin Ubaid, Muhammad Irfan, Hanan T Halawani, Aisha M Mashraqi, Sabah Elshafie Mohammed Elshafie, Abdullah A Asiri, Muawia Abdelkafi Magzoub

  • March 1, 2026

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