Deep Learning to Improve Breast Cancer Detection on Screening Mammography

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an "end-to-end" training approach that efficiently leverages training datasets with either complete clinical annotation or only the cancer status (label) of the whole image. In this approach, lesion annotations are required only in the initial training stage, and subsequent stages require only image-level labels, eliminating the reliance on rarely available lesion annotations. Our all convolutional network method for classifying screening…

• IS
  Icahn School of Medicine at Mount Sinai

  Award: P30CA196521
• NI
  National Institutes of Health

  Awards: NIH P30CA196521, P30CA196521, UL1TR001433