Slide Level

Slide-level classification of whole slide images (WSIs) in digital pathology aims to predict diagnostic labels for entire slides, avoiding the laborious task of individual patch annotation. Current research heavily utilizes multiple instance learning (MIL) approaches, often incorporating transformer-based architectures and self-supervised learning to improve feature extraction from image patches and their aggregation into slide-level representations. This focus on efficient and accurate slide-level prediction is crucial for accelerating diagnostic workflows and improving the efficiency of computational pathology, particularly in scenarios with limited annotated data. The development of robust and generalizable models is driving progress towards more reliable and clinically impactful AI-driven pathology.