The new type of coronavirus pneumonia (COVID-19) is spreading around the world, and one of the main reasons is the low detection efficiency and lack of detection materials. Image recognition algorithms based on deep learning are an effective method to assist epidemic detection. However, COVID-19 medical image data has the problems of difficulty in obtaining case samples and high data labeling costs. Contrastive learning algorithm is the best performing method among self-supervised learning algorithms without label training. To solve the problem of insufficient annotation data for COVID-19 medical images, this paper proposes a contrastive learning algorithm that incorporates feature synthesis components of hard negative samples. The feature of hard negative samples is a key factor to promote network training among a large number of negative samples features. Generating more hard negative samples features and adding them to the contrastive loss function for calculation allows the comparison learning algorithm to learn better semantic features. At the same time, the contrastive learning algorithm proposed in this paper is used in the transfer learning of COVID-19 CT images, which can deal with the problem of large differences between the original domain and the target domain. The algorithm proposed in this paper uses common methods of self-supervised learning to evaluate the classification accuracy of 0.798 on the COVID-CT data set, which has a significant improvement. The training method of transfer learning has better classification performance than common methods, and the classification accuracy is 0.852. © 2021 ACM.
Fast Mixing of Hard Negative Samples for Contrastive Learning and Use for COVID-19