  Medical image datasets often exhibit long-tailed distributions due to the
inherent challenges in medical data collection and annotation. In long-tailed
contexts, some common disease categories account for most of the data, while
only a few samples are available in the rare disease categories, resulting in
poor performance of deep learning methods. To address this issue, previous
approaches have employed class re-sampling or re-weighting techniques, which
often encounter challenges such as overfitting to tail classes or difficulties
in optimization during training. In this work, we propose a novel approach,
namely \textbf{S}aliency-guided and \textbf{P}atch-based \textbf{Mix}up (SPMix)
for long-tailed skin cancer image classification. Specifically, given a
tail-class image and a head-class image, we generate a new tail-class image by
mixing them under the guidance of saliency mapping, which allows for preserving
and augmenting the discriminative features of the tail classes without any
interference of the head-class features. Extensive experiments are conducted on
the ISIC2018 dataset, demonstrating the superiority of SPMix over existing
state-of-the-art methods.
