WSI Representation

Whole slide image (WSI) representation focuses on efficiently converting massive digital pathology images into compact, informative vectors for downstream tasks like image search and classification. Current research emphasizes developing methods that move beyond patch-based approaches to capture holistic slide-level information, employing techniques like graph neural networks to leverage inter-slide correlations and self-supervised learning with masked autoencoders for pre-training. These advancements aim to improve the accuracy and efficiency of computational pathology, addressing challenges in storage and computational cost while enabling more robust and generalized analysis of WSIs for improved cancer diagnostics and research.