Medical image classifiers often suffer from the imbalanced class distribution of datasets. For
example, among the 7 classes in the ISIC2018 skin lesion detection dataset, over 67% of the
instances belong to melanocytic nevus while only 1% belong to dermatofibroma. Contrastive
feature learning has been shown to achieve promising results in enhancing the performance
for imbalanced classification tasks. However, the contrastive learning methods are either not
end-to-end or require extra memory, which may lead to less compatible and sub-optimal …

Medical image classifiers often suffer from the imbalanced class distribution of datasets. For
example, among the 7 classes in the ISIC2018 skin lesion detection dataset, over 67% of the
instances belong to melanocytic nevus while only 1% belong to dermatofibroma. Contrastive
feature learning has been shown to achieve promising results in enhancing the performance
for imbalanced classification tasks. However, the contrastive learning methods are either not
end-to-end or require extra memory, which may lead to less compatible and sub-optimal …