  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.
