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Large-Scale Mammography CAD with Deformable Conv-Nets

  • Stephen MorrellEmail author
  • Zbigniew Wojna
  • Can Son Khoo
  • Sebastien Ourselin
  • Juan Eugenio Iglesias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules. Among them, region-based fully convolutional networks (R-FCN) and deformable convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for speed and low consumption of memory, which is crucial for processing the high resolutions of to \(50\,\upmu \hbox {m}\) used by radiologists. Deformable convolution and pooling can model a wide range of mammographic findings of different morphology and scales, thanks to their versatility. In this study, we present a neural net architecture based on R-FCN/DCN, that we have adapted from the natural image domain to suit mammograms—particularly their larger image size—without compromising resolution. We trained the network on a large, recently released dataset (Optimam) including 6,500 cancerous mammograms. By combining our modern architecture with such a rich dataset, we achieved an area under the ROC curve of 0.879 for breast-wise detection in the DREAMS challenge (130,000 withheld images), which surpassed all other submissions in the competitive phase.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Stephen Morrell
    • 1
    Email author
  • Zbigniew Wojna
    • 1
  • Can Son Khoo
    • 1
  • Sebastien Ourselin
    • 2
  • Juan Eugenio Iglesias
    • 1
  1. 1.Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
  2. 2.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK

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