ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification. (arXiv:2209.00183v1 [cs.CV])

Medical image classification has been widely adopted in medical image
analysis. However, due to the difficulty of collecting and labeling data in the
medical area, medical image datasets are usually highly-imbalanced. To address
this problem, previous works utilized class samples as prior for re-weighting
or re-sampling but the feature representation is usually still not
discriminative enough. In this paper, we adopt the contrastive learning to
tackle the long-tailed medical imbalance problem. Specifically, we first
propose the category prototype and adversarial …

arxiv classification image learning medical