BPaCo: Balanced Parametric Contrastive Learning for Long-Tailed Medical Image Classification

Zhiyuan Cai, Tianyunxi Wei, Li Lin, Hao Chen, Xiaoying Tang

Abstract: Medical image classification is an essential medical image analysis tasks. However, due to data scarcity of rare diseases in clinical scenarios, the acquired medical image datasets may exhibit long-tailed distributions. Previous works employ class re-balancing to address this issue yet the representation is usually not discriminative enough. Inspired by contrastive learning’s power in representation learning, in this paper, we propose and validate a contrastive learning based framework, named Balanced Parametric Contrastive learning (BPaCo), to tackle long-tailed medical image classification. There are three key components in BPaCo: across-batch class-averaging to balance the gradient contribution from negative classes; hybrid class-complement to have all classes appear in every mini-batch for discriminative prototypes; cross-entropy logit compensation to formulate an end-to-end classification framework with even stronger feature representations. Our BPaCo shows outstanding classification performance and high computational efficiency on three highly-imbalanced medical image classification datasets. The source code is available at https://github.com/Davidczy/BPaCo.