Weakly Supervised Deep Learning Approach to Breast MRI Assessment.

Journal: Academic radiology
Published Date: Jun 6, 2021

Abstract

RATIONALE AND OBJECTIVES: To evaluate a weakly supervised deep learning approach to breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in order to improve the specificity of breast MRI lesion classification.

Authors

  • Michael Z Liu
    Department of Radiology, Columbia University Medical Center, New York, NY.
  • Cara Swintelski
    Department of Radiology, Columbia University Medical Center, New York, NY 10032.
  • Shawn Sun
    Columbia University Medical Center, New York Presbyterian Hospital, 622 West 168th Street, PB-1-301, New York, NY 10032, United States of America. Electronic address: shs2179@cumc.columbia.edu.
  • Maham Siddique
    Department of Radiology, Columbia University Medical Center, New York, NY.
  • Elise Desperito
    Department of Radiology, Columbia University Medical Center, New York, NY 10032.
  • Sachin Jambawalikar
    Department of Radiology, Columbia University Medical Center, New York, NY.
  • Richard Ha
    Department of Radiology, Columbia University Medical Center, New York, NY.

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