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Deep Learning in Breast Cancer Screening

  • Hugh HarveyEmail author
  • Andreas Heindl
  • Galvin Khara
  • Dimitrios Korkinof
  • Michael O’Neill
  • Joseph Yearsley
  • Edith Karpati
  • Tobias Rijken
  • Peter Kecskemethy
  • Gabor Forrai
Chapter
First Online:

Abstract

Traditional computer aided detection (CAD) systems for breast cancer screening relied on machine learning with human-coded feature-engineering. They have largely failed to fulfill the promise of improving screening accuracy and workflow efficiency, and are often associated with increased recall rates and avoidable screening costs due to high instances of false positive markings. Advances in machine learning (such as deep learning) are on the cusp of providing more effective, more efficient, and even more patient-centric breast cancer screening support than ever before. By leveraging the consistent high sensitivity and specificity performance of autonomous systems, in combination with expert human oversight, the potential for efficient single-reader software-supported screening programs with low recall rates is on the horizon.

Keywords

Breast cancer Screening Mammography CAD Deep learning 
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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hugh Harvey
    • 1
    Email author
  • Andreas Heindl
    • 1
  • Galvin Khara
    • 1
  • Dimitrios Korkinof
    • 1
  • Michael O’Neill
    • 1
  • Joseph Yearsley
    • 1
  • Edith Karpati
    • 1
  • Tobias Rijken
    • 1
  • Peter Kecskemethy
    • 1
  • Gabor Forrai
    • 2
  1. 1.Kheiron Medical TechnologiesLondonUK
  2. 2.European Society of Breast ImagingViennaAustria

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