Saliency-Guided and Patch-Based Mixup for Long-Tailed Skin Cancer Image Classification

作者：Tianyunxi Wei, Yijin Huang, Li Lin, Pujin Cheng, Sirui Li, Xiaoying Tang · 年份：2024 · DOI：10.1109/isbi56570.2024.10635293 · 被引用次数：1 · 研究领域：Visual Attention and Saliency Detection、Cutaneous Melanoma Detection and Management、Advanced Neural Network Applications

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 Saliency-guided and Patch-based Mixup (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. The source code is available at https://github.com/Yancy10-1/SPMix.