  Deep learning can promote the 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
(TSBNL) framework is proposed to improve the performance of classification
network with limited mammograms. In particular, a new gray-scale image mapping
(GSIM) task for image restoration is designed as the pretext task to improve
discriminate feature representation with label information of mammograms. The
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 diagnosis of breast cancers
with limited samples.
