# Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

arXiv: https://arxiv.org/abs/1907.00300 · HTML: https://mlsanity.com/paper/1907.00300/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [Generative models improve fairness of medical classifiers under distribution shifts](https://mlsanity.com/paper/2304.09218/) (2023)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica](https://mlsanity.com/paper/2107.11696/) (2021)
- [Domain Invariant Model with Graph Convolutional Network for Mammogram Classification](https://mlsanity.com/paper/2204.09954/) (2022)

## Abstract (arXiv)

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 textscDiagNet. 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 textscDiagNet framework outperforms the state-of-the-art in breast mass diagnosis in mammography.