TopoSlide: Topologically-Informed Histopathology Whole Slide Image Representation Learning

Histopathology whole slide images (WSIs) are gigapixel images that present significant challenges in generating effective representations that capture both local histological features and their global spatial organization. Current pathology foundation models focus primarily on local patch-level features while neglecting the complex spatial relationships that pathologists rely on for diagnosis and prognosis. We introduce TopoSlide, a novel self-supervised representation learning framework that leverages persistent homology from topological data analysis to capture the global spatial organization of tissue architecture in WSIs.

Our method decomposes slides into histologically meaningful clusters using patch-level embeddings, then characterizes their spatial arrangement through topological descriptors. We train a vision transformer to predict cluster topology from slide-level embeddings using a conditional multitask objective that integrates local patch features with their topological attributes. Evaluated across lung adenocarcinoma and breast cancer cohorts, TopoSlide achieves superior performance improving histologic pattern retrieval by up to 15% in majority voting macro F1 score, and competitive survival and gene mutation predictions, while training on only hundreds of slides compared to hundreds of thousands for foundation models. Our results demonstrate that topology-aware learning provides a powerful inductive bias for pathology representation learning, enabling both improved performance and novel topology-based conditional retrieval capabilities for clinical applications. Our code and models are publicly available. 1