Skin image datasets often suffer from imbalanced data distribution,
exacerbating the difficulty of computer-aided skin disease diagnosis. Some
recent works exploit supervised contrastive learning (SCL) for this long-tailed
challenge. Despite achieving significant performance, these SCL-based methods
focus more on head classes, yet ignoring the utilization of information in tail
classes. In this paper, we propose class-Enhancement Contrastive Learning
(ECL), which enriches the information of minority classes and treats different
classes equally. For information enhancement, we design a hybrid-proxy model to
generate class-dependent proxies and propose a cycle update strategy for
parameters optimization. A balanced-hybrid-proxy loss is designed to exploit
relations between samples and proxies with different classes treated equally.
Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into
account, we further present a balanced-weighted cross-entropy loss following
curriculum learning schedule. Experimental results on the classification of
imbalanced skin lesion data have demonstrated the superiority and effectiveness
of our method.

通过类增强对比学习方法，使用平衡的代理模型和参数优化策略，配合平衡的加权交叉熵损失函数，对于不平衡肌肤病变数据的分类问题进行了实验，证明了该方法的卓越性和有效性。