Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification
This addresses a critical challenge for medical AI practitioners by providing a more robust method for handling noisy labels in histopathology, though it is incremental as it builds on existing foundation models.
The paper tackles the problem of label noise in histopathology image classification by showing that embeddings from self-supervised contrastive foundation models improve noise resilience, outperforming non-contrastive methods and other noise-resilient techniques in empirical analyses across multiple datasets.
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.