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.Andthe 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.
关键词
医学图像/图像分类/自监督学习/深度学习
Key words
medical image/image classification/self-supervised learning/deep learning
维普
万方数据