  Recent advancements in deep learning have proven highly effective in medical
image classification, notably within histopathology. However, noisy labels
represent a critical challenge in histopathology image classification, where
accurate annotations are vital for training robust deep learning models.
Indeed, deep neural networks can easily overfit label noise, leading to severe
degradations in model performance. While numerous public pathology foundation
models have emerged recently, none have evaluated their resilience to label
noise. Through thorough empirical analyses across multiple datasets, we exhibit
the label noise resilience property of embeddings extracted from foundation
models trained in a self-supervised contrastive manner. We demonstrate that
training with such embeddings substantially enhances label noise robustness
when compared to non-contrastive-based ones as well as commonly used
noise-resilient methods. Our results unequivocally underline the superiority of
contrastive learning in effectively mitigating the label noise challenge. Code
is publicly available at
https://github.com/LucasDedieu/NoiseResilientHistopathology.
