Abstract: Medical image classification is an important method to determine the illness of patients and give corresponding treatment advice. As medical image labeling requires relevant professional knowledge, it is difficult to obtain large-scale medical image classification labels. And the development of medical image classification based on deep learning method is limited to some extent. To solve this problem, self-supervised contrast learning is applied to medical image classification tasks in this paper. Contrast learning method is used in pre-training of medical image classification. The features are learned from unlabeled medical images in the pre-training stage to provide prior knowledge for subsequent medical image classification. Experimental results show that the proposed improved method of medical image classification based on self-supervised contrast learning enhances the classification performance of the ResNet. 

Key words: medical image, image classification, self-supervised learning, deep learning