Projects per year
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 \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 language | Undefined/Unknown |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 |
Subtitle of host publication | 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI |
Publisher | Springer |
Pages | 486-494 |
DOIs | |
Publication status | Published - 10 Oct 2019 |
Event | 22nd International Conference on Medical Image Computing and Computer Assisted Intervention - InterContinental Shenzhen, Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019 Conference number: 22 https://www.miccai2019.org/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 11769 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 22nd International Conference on Medical Image Computing and Computer Assisted Intervention |
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Abbreviated title | MICCAI 2019 |
Country/Territory | China |
City | Shenzhen |
Period | 13/10/19 → 17/10/19 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- deep learning
- Mammography Diagnosis
- adversarial learning
- graph regularization
Projects
- 1 Finished
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C-SENSE: Exploiting low dimensional models in sensing, computation and signal processing
1/09/16 → 31/08/22
Project: Research