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Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

  • Heyi LiEmail author
  • Dongdong Chen
  • William H. Nailon
  • Mike E. Davies
  • David I. Laurenson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses. To address this, we propose a signed graph regularized deep neural network with adversarial augmentation, named DiagNet. Firstly, we use adversarial learning to generate positive and negative mass-contained mammograms for each mass class. After that, a signed similarity graph is built upon the expanded data to further highlight the discrimination. Finally, a deep convolutional neural network is trained by jointly optimizing the signed graph regularization and classification loss. Experiments show that the DiagNet framework outperforms the state-of-the-art in breast mass diagnosis in mammography.

Keywords

Deep learning Mammography diagnosis Adversarial learning Graph regularization 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Heyi Li
    • 1
    Email author
  • Dongdong Chen
    • 1
  • William H. Nailon
    • 2
  • Mike E. Davies
    • 1
  • David I. Laurenson
    • 1
  1. 1.Institute for Digital CommunicationsUniversity of EdinburghEdinburghUK
  2. 2.Oncology Physics DepartmentEdinburgh Cancer Centre, Western General HospitalEdinburghUK

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