  Computer-aided breast cancer diagnosis in mammography is a challenging
problem, stemming from mammographical data scarcity and data entanglement. In
particular, data scarcity is attributed to the privacy and expensive
annotation. And data entanglement is due to the high similarity between benign
and malignant masses, of which manifolds reside in lower dimensional space with
very small margin. To address these two challenges, we propose a deep learning
framework, named Contrastive Identifier Network (\textsc{COIN}), which
integrates adversarial augmentation and manifold-based contrastive learning.
Firstly, we employ adversarial learning to create both on- and off-distribution
mass contained ROIs. After that, we propose a novel contrastive loss with a
built Signed graph. Finally, the neural network is optimized in a contrastive
learning manner, with the purpose of improving the deep model's
discriminativity on the extended dataset. In particular, by employing COIN,
data samples from the same category are pulled close whereas those with
different labels are pushed further in the deep latent space. Moreover, COIN
outperforms the state-of-the-art related algorithms for solving breast cancer
diagnosis problem by a considerable margin, achieving 93.4\% accuracy and
95.0\% AUC score. The code will release on ***.
