Balanced And Discriminative Contrastive Learning For Class-Imbalanced Medical Images

The class imbalance problem, which is prevalent in medical image datasets, seriously affects the diagnostic effectiveness of deep learning-based network models. Recently, the method based on two-stage learning has produced promising results in solving class imbalance. In two-stage learning, the learning of unbiased classifiers has been well studied, but the representation of imbalanced data is still being explored. In this paper, we focus on the representation learning stage of class-imbalanced and propose a novel balanced and discriminative contrastive learning (BDCL) method. Compared with supervised contrastive learning, BDCL has two improvements: temperature dynamic learning, which balances the gradient contribution of negative samples from different classes; hard example prototypes, which can better learn the feature differences between tail classes. A variety of experimental results on the imbalanced medical image datasets show that BDCL enables the network models to learn representations with desirable balancedness and discriminativeness, effectively solving the class imbalance problem.