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.

医学图像分类中存在嘈杂标签会对深度学习产生重大影响，自监督预训练可以提高对嘈杂标签的鲁棒性，但这种鲁棒性受诸如类别数目、数据集复杂性和训练规模等因素的影响。本研究全面探讨了自监督学习与医学图像分类中嘈杂标签鲁棒性之间的相互作用，结果显示在五个数据集中（Fetal plane, DermNet, COVID-DU-Ex, MURA, NCT-CRC-HE-100K），DermNet是最具挑战性的数据集，但对嘈杂标签表现出更强的鲁棒性，而对于嘈杂标签的鲁棒性增强方面，对比学习是最有效的自监督方法。

自监督预训练如何提升在不同医学图像分类数据集中对噪声标签的鲁棒性？