A Good Feature Extractor Is All You Need for Weakly Supervised Learning in Histopathology

Deep learning is revolutionising pathology, offering novel opportunities in disease prognosis and personalised treatment. Historically, stain normalisation has been a crucial preprocessing step in computational pathology pipelines, and persists into the deep learning era. Yet, with the emergence of feature extractors trained using self-supervised learning (SSL) on diverse pathology datasets, we call this practice into question. In an empirical evaluation of publicly available feature extractors, we find that omitting stain normalisation and image augmentations does not compromise downstream performance, while incurring substantial savings in memory and compute. Further, we show that the top-performing feature extractors are remarkably robust to variations in stain and augmentations like rotation in their latent space. Contrary to previous patch-level benchmarking studies, our approach emphasises clinical relevance by focusing on slide-level prediction tasks in a weakly supervised setting with external validation cohorts. This work represents the most comprehensive robustness evaluation of public pathology SSL feature extractors to date, involving more than 6,000 training runs across nine tasks, five datasets, three downstream architectures, and various preprocessing setups. Our findings stand to streamline digital pathology workflows by minimising preprocessing needs and informing the selection of feature extractors.