Self-supervision Adversarial Learning Network for Liver Lesion Classification

The lack of training samples is one of the main factors affecting the development of deep learning methods. Deep learning models often fail to learn useful features and have serious over-fitting problems when lacking of training data. In this work, we exploit two popular unsupervised learning techniques: adversarial learning and self-supervised learning, which is aimed at mine more useful representations and relieve over-fitting problems. Our training scheme is mainly divided into three steps. Firstly, we train a self-supervision network with unsupervised learning to extract obvious features from our liver lesion samples and these features will be transferred to next step. Secondly, we use the final output feature map generated by self-supervision network to train a discriminator by adversarial learning. Finally, the backbone network is trained under the constraint of discriminator and classifier. Our main idea is to train a discriminator with adversarial learning and self-supervised learning. Then, we use the discriminator to constrain the backbone network, which is aimed to reduce the backbone network solution search space. In particular, Different from generating data with GAN, we use GAN to feature adversarial learning for feature augmentation. Our experiments on liver lesion classification in CT show an average accuracy as 92.51% compared with the baseline training scheme, which demonstrates our proposed method can mime useful features and relieve over-fitting problem. It can assist physicians in the early detection and treatment of liver lesions.