Saliency-guided and Patch-based Mixup for Long-tailed Skin Cancer Image Classification
This work addresses the challenge of imbalanced medical datasets for skin cancer diagnosis, offering an incremental improvement over prior techniques.
The paper tackles the problem of long-tailed distributions in skin cancer image classification by proposing SPMix, a saliency-guided and patch-based mixup method, which improves performance on the ISIC2018 dataset over existing state-of-the-art approaches.
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.