# Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification Source: [Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:36:44.860Z Authors: Lucas Dedieu, Nicolas Nerrienet, Adrien Nivaggioli, Clara Simmat, Marceau Clavel, Arnaud Gauthier, St\\'ephane Sockeel, R\\'emy Peyret Published: 2024-04-11T09:47:52.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()