  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.
