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.

翻译：深层学习可以促进乳癌的乳房X线摄影计算机辅助诊断(CAD),但通常会受到规模小的抽样问题的影响。在这项工作中,提议了一个由任务驱动的自我监督双通道网络(TSBNL)框架,以提高使用有限的乳房X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线的图像恢复任务,作为改善乳房X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线X线xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxmlxmlxmlxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx