Noisy labels can significantly impact medical image classification, particularly in deep learning, by corrupting learned features. Self-supervised pretraining, which doesn't rely on labeled data, can enhance robustness against noisy labels. However, this robustness varies based on factors like the number of classes, dataset complexity, and training size. In medical images, subtle inter-class differences and modality-specific characteristics add complexity. Previous research hasn't comprehensively explored the interplay between self-supervised learning and robustness against noisy labels in medical image classification, considering all these factors. In this study, we address three key questions: i) How does label noise impact various medical image classification datasets? ii) Which types of medical image datasets are more challenging to learn and more affected by label noise? iii) How do different self-supervised pretraining methods enhance robustness across various medical image datasets? Our results show that DermNet, among five datasets (Fetal plane, DermNet, COVID-DU-Ex, MURA, NCT-CRC-HE-100K), is the most challenging but exhibits greater robustness against noisy labels. Additionally, contrastive learning stands out among the eight self-supervised methods as the most effective approach to enhance robustness against noisy labels.

翻译：噪声标签会显著影响医学图像分类，尤其是在深度学习中，其会破坏学习到的特征。不依赖标注数据的自监督预训练，能够提升对噪声标签的鲁棒性。然而，这种鲁棒性受类别数量、数据集复杂度及训练规模等因素影响。在医学图像中，类间细微差异及模态特定特征增加了复杂性。以往研究尚未综合考虑这些因素，全面探明自监督学习与医学图像分类中噪声标签鲁棒性之间的相互作用。本研究聚焦三个关键问题：i) 标签噪声如何影响不同医学图像分类数据集？ii) 哪些类型的医学图像数据集更难学习且受标签噪声影响更大？iii) 不同的自监督预训练方法如何提升不同医学数据集对噪声的鲁棒性？结果表明，在五个数据集（Fetal plane、DermNet、COVID-DU-Ex、MURA、NCT-CRC-HE-100K）中，DermNet最具挑战性，但对噪声标签的鲁棒性更强。此外，八种自监督方法中，对比学习是增强噪声标签鲁棒性最有效的方法。