Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

Heyi Li*, Dongdong Chen, William H. Nailon, Mike E. Davies, David I. Laurenson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 \textsc{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 \textsc{DiagNet} framework outperforms the state-of-the-art in breast mass diagnosis in mammography.
Original languageUndefined/Unknown
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019
Subtitle of host publication22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI
Publication statusPublished - 10 Oct 2019
Event22nd International Conference on Medical Image Computing and Computer Assisted Intervention - InterContinental Shenzhen, Shenzhen, China
Duration: 13 Oct 201917 Oct 2019
Conference number: 22

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2019
Internet address

Keywords / Materials (for Non-textual outputs)

  • deep learning
  • Mammography Diagnosis
  • adversarial learning
  • graph regularization

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