Improving Workflow Efficiency for Mammography Using Machine Learning.

Journal: Journal of the American College of Radiology : JACR
Published Date: Jan 1, 2020

Abstract

OBJECTIVE: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and abnormal examinations for radiological interpretation.

Authors

  • Trent Kyono
    Department of Computer Science, University of California Los Angeles, Los Angeles, California. Electronic address: tmkyono@gmail.com.
  • Fiona J Gilbert
    Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom; NIHR Cambridge Biomedical Research Center, Cambridge, United Kingdom.
  • Mihaela van der Schaar
    University of California, Los Angeles, CA, USA.

Keywords

External Resources

Popular Topics
Recent Journals