  Medical image classification has been widely adopted in medical image
analysis. However, due to the difficulty of collecting and labeling data in the
medical area, medical image datasets are usually highly-imbalanced. To address
this problem, previous works utilized class samples as prior for re-weighting
or re-sampling but the feature representation is usually still not
discriminative enough. In this paper, we adopt the contrastive learning to
tackle the long-tailed medical imbalance problem. Specifically, we first
propose the category prototype and adversarial proto-instance to generate
representative contrastive pairs. Then, the prototype recalibration strategy is
proposed to address the highly imbalanced data distribution. Finally, a unified
proto-loss is designed to train our framework. The overall framework, namely as
Prototype-aware Contrastive learning (ProCo), is unified as a single-stage
pipeline in an end-to-end manner to alleviate the imbalanced problem in medical
image classification, which is also a distinct progress than existing works as
they follow the traditional two-stage pipeline. Extensive experiments on two
highly-imbalanced medical image classification datasets demonstrate that our
method outperforms the existing state-of-the-art methods by a large margin.
