Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image Classification

arXiv:2404.07605v1 Announce Type: new
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 …

arxiv classification cs.ai cs.cv embeddings image noise resilient type