  Deep learning can promote the mammography-based computer-aided diagnosis
(CAD) for breast cancers, but it generally suffers from the small sample size
problem. Self-supervised learning (SSL) has shown its effectiveness in medical
image analysis with limited training samples. However, the network model
sometimes cannot be well pre-trained in the conventional SSL framework due to
the limitation of the pretext task and fine-tuning mechanism. In this work, a
Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to
improve the performance of classification model the mammography-based CAD. In
particular, a new gray-scale image mapping (GSIM) is designed as the pretext
task, which embeds the class label information of mammograms into the image
restoration task to improve discriminative feature representation. The proposed
TSBN then innovatively integrates different network architecture, including the
image restoration network and the classification network, into a unified SSL
framework. It jointly trains the bi-channel network models and collaboratively
transfers the knowledge from the pretext task network to the downstream task
network with improved diagnostic accuracy. The proposed TSBN is evaluated on a
public INbreast mammogram dataset. The experimental results indicate that it
outperforms the conventional SSL and multi-task learning algorithms for
diagnosis of breast cancers with limited samples.
