A self-supervised framework for learning whole slide representations

Whole slide imaging is fundamental to biomedical microscopy and computational pathology. However, whole slide images (WSIs) present a complex computer vision challenge due to their gigapixel size, diverse histopathologic features, spatial heterogeneity, and limited/absent data annotations. These challenges highlight that supervised training alone can result in suboptimal whole slide representations. Self-supervised representation learning can achieve high-quality WSI visual feature learning for downstream diagnostic tasks, such as cancer diagnosis or molecular genetic prediction. Here, we present a general self-supervised whole slide …

annotations biomedical challenge challenges computational computer computer vision cs.ai cs.cv cs.lg data diverse features framework highlight images imaging microscopy pathology representation spatial supervised training training vision