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

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.