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学术动态：TailBoost: Tail-Synthetic Learning for Boosting Long-Tailed Skin Cancer Image Classification
论文标题：TailBoost: Tail-Synthetic Learning for Boosting Long-Tailed Skin Cancer Image Classification
发布日期：2026-05-25
作者：Tianyunxi Wei, Yijin Huang, Li Lin, Pujin Cheng, Xiaoying Tang
DOI：10.3390/s26113343
论文摘要：Skin cancer image data often exhibit long-tailed distributions due to the inherent challenges in data collection and annotation. Specifically, a few predominant classes dominate a dataset of interest, while minority classes, referred to as tail classes, are underrepresented with only limited numbers of samples. Such imbalance is highly likely to adversely affect the performance of deep learning models. To address this issue, previous methods employ mixup techniques to synthesize tail-class images, thereby attempting to balance the training data. However, traditional mixup methods typically do not specifically pay attention to specific regions of interest, blending two images with indistinction between objects of interest and background. Such disregard for important semantic features may result in synthetic samples with broken or distorted diagnostic features. In this work, we introduce a novel framework, the Tail-synthetic Learning for Boosting Long-tailed Skin Cancer Image Classification (TailBoost) framework. Our approach generates a new tail-class image by combining a tail-class image with a head-class image under the guidance of their corresponding saliency maps. This strategy, namely SPMix, preserves and enhances the discriminative features of the tail-class image with minimum interference from the head-class image. We further refine the learned representations by incorporating supervised contrastive learning with class-center rebalance. Extensive experiments on the ISIC2018, ISIC2019, and PAD-UFES-20 datasets demonstrate that TailBoost outperforms existing state-of-the-art long-tailed learning methods.
元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113343. Vol. 26, Issue 11. Authors: Tianyunxi Wei, Yijin Huang, Li Lin, Pujin Cheng, Xiaoying Tang.
开放许可：https://creativecommons.org/licenses/by/4.0/
原文链接：https://doi.org/10.3390/s26113343
PDF 链接：https://www.mdpi.com/1424-8220/26/11/3343/pdf