Task-Driven Self-Supervised BI-Channel Networks Learning for Diagnosis of Breast Cancers with Mammography

Deep learning can promote mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small size sample problem. In this work, a task-driven self-supervised bi-channel networks learning (TSBNL) framework is proposed to improve the network performance with limited mammograms. In particular, a new gray-scale image mapping (GSIM) task for image restoration is designed as the pretext task to improve discriminative feature representation with label information of mammograms. TSBNL then innovatively integrates this image restoration network and the downstream classification network into a unified SSL framework, and transfers the knowledge from the pretext network to the classification network with improved diagnostic accuracy. The proposed algorithm is evaluated on a public INbreast mammogram dataset. The experimental results indicate that it outperforms the conventional SSL algorithms for the diagnosis of breast cancers with limited samples.