ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification

• URL: http://arxiv.org/abs/2307.04136v1
• Date: Sun, 9 Jul 2023 09:29:15 GMT
• Title: ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification
• Authors: Yilan Zhang, Jianqi Chen, Ke Wang, Fengying Xie
• Abstract summary: Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. We propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally.
• Score: 7.7379419801373475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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