Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
Published Date: Dec 1, 2018

Abstract

PURPOSE: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish recalled but benign mammography images from negative exams and those with malignancy.

Authors

  • Sarah S Aboutalib
    Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Aly A Mohamed
    Department of Radiology, University of Pittsburgh School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
  • Wendie A Berg
    Department of Radiology, University of Pittsburgh School of Medicine, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA.
  • Margarita L Zuley
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Jules H Sumkin
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
  • Shandong Wu
    Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.

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