HyperPath: Knowledge-Guided Hyperbolic Semantic Hierarchy Modeling for WSI Analysis

Peixiang Huang, Yanyan Huang, Weiqin Zhao, Junjun He, Lequan Yu

Abstract:

Pathology is essential for cancer diagnosis, with multiple instance learning (MIL) widely used for whole slide image (WSI) analysis.
WSIs exhibit a natural hierarchy—patches, regions, and slides—with distinct semantic associations. While some methods attempt to leverage this
hierarchy for improved representation, they predominantly rely on Euclidean embeddings, which struggle to fully capture semantic hierarchies.
To address this limitation, we propose HyperPath, a novel method that
integrates knowledge from textual descriptions to guide the modeling
of semantic hierarchies of WSIs in hyperbolic space, thereby enhancing
WSI classification. Our approach adapts both visual and textual features
extracted by pathology vision-language foundation models to the hyperbolic space. We design an Angular Modality Alignment Loss to ensure
robust cross-modal alignment, while a Semantic Hierarchy Consistency
Loss further refines feature hierarchies through entailment and contradiction relationships and thus enhance semantic coherence. The classification is performed with geodesic distance, which measures the similarity
between entities in the hyperbolic semantic hierarchy. This eliminates the
need for linear classifiers and enables a geometry-aware approach to WSI
analysis. Extensive experiments show that our method achieves superior
performance across tasks compared to existing methods, highlighting the
potential of hyperbolic embeddings for WSI analysis.