bibtext: @InProceedings{ Cai_BPaCo_MICCAI2024,
                   author = { Cai, Zhiyuan and Wei, Tianyunxi and Lin, Li and Chen, Hao and Tang, Xiaoying },
                   title = { { BPaCo: Balanced Parametric Contrastive Learning for Long-tailed Medical Image Classification } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
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.
title: BPaCo: Balanced Parametric Contrastive Learning for Long-tailed Medical Image Classification
authors: Cai, Zhiyuan Wei, Tianyunxi Lin, Li Chen, Hao Tang, Xiaoying