Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion.

Journal: Medical physics
Published Date: Jan 16, 2019

Abstract

PURPOSE: We propose a deep learning-based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment protocol.

Authors

  • Michał Byra
    Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106, Warsaw, Poland. mbyra@ippt.pan.pl.
  • Michael Galperin
    Almen Laboratories, Inc., 1672 Gil Way, Vista, CA, 92084, USA.
  • Haydee Ojeda-Fournier
    Department of Radiology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
  • Linda Olson
    Department of Radiology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
  • Mary O'Boyle
    Department of Radiology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
  • Christopher Comstock
    Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
  • Michael Andre
    Department of Radiology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.

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