Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date: Jun 22, 2019

Abstract

BACKGROUND: To determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.

Authors

  • Benjamin Hinton
    Department of Bioengineering, University of California-San Francisco Berkeley Joint Program, Room A-C106-B, 1 Irving St, San Francisco, CA, 94143, USA. bhinton@berkeley.edu.
  • Lin Ma
    Department of Radiation Oncology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States.
  • Amir Pasha Mahmoudzadeh
    Accenture, San Francisco, CA, 94143, USA.
  • Serghei Malkov
    Applied Materials, Santa Clara, CA, USA.
  • Bo Fan
    Department of Bioengineering, University of California-San Francisco Berkeley Joint Program, Room A-C106-B, 1 Irving St, San Francisco, CA, 94143, USA.
  • Heather Greenwood
    Department of Radiology and Biomedical Imaging, UC-San Francisco, San Francisco, CA, 94143, USA.
  • Bonnie Joe
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
  • Vivian Lee
    Research Advocate, UCSF Breast Science Advocacy Core, San Francisco, CA, 94143, USA.
  • Karla Kerlikowske
    Departments of Medicine and Epidemiology and Biostatistics, UCSF, San Francisco, CA, 94143, USA.
  • John Shepherd
    Cancer Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA.

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