Slide Representation

Slide representation in computational pathology focuses on creating effective numerical summaries of whole-slide images (WSIs), enabling efficient analysis of gigapixel-sized histological data. Current research emphasizes self-supervised learning methods, often employing contrastive learning or prototype-based approaches, and exploring multimodal integration with transcriptomic data or clinical reports to generate robust and generalizable slide-level representations. These advancements are crucial for improving diagnostic accuracy, prognostic prediction, and the development of AI-driven clinical decision support systems in pathology. The resulting models show promise in various downstream tasks, including cancer subtyping, biomarker prediction, and even estimating inter-pathologist diagnostic concordance.