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Computer-aided breast cancer diagnosis in mammography is a challengingproblem, stemming from mammographical data scarcity and data entanglement. Inparticular, data scarcity is attributed to the privacy and expensiveannotation. And data entanglement is due to the high similarity between benignand malignant masses, of which manifolds reside in lower dimensional space withvery small margin. To address these two challenges, we propose a deep learningframework, named Contrastive Identifier Network (\textsc{COIN}), whichintegrates adversarial augmentation and manifold-based contrastive learning.Firstly, we employ adversarial learning to create both on- and off-distributionmass contained ROIs. After that, we propose a novel contrastive loss with abuilt Signed graph. Finally, the neural network is optimized in a contrastivelearning manner, with the purpose of improving the deep model'sdiscriminativity on the extended dataset. In particular, by employing COIN,data samples from the same category are pulled close whereas those withdifferent labels are pushed further in the deep latent space. Moreover, COINoutperforms the state-of-the-art related algorithms for solving breast cancerdiagnosis problem by a considerable margin, achieving 93.4\% accuracy and95.0\% AUC score. The code will release on ***.

COIN: Contrastive Identifier Network for Breast Mass Diagnosis in Mammography