标题： HyperPath：基于知识引导的双曲语义层次结构建模用于WSI分析 显示英文标题

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

摘要： 病理学对于癌症诊断至关重要，多实例学习（MIL）被广泛用于全切片图像（WSI）分析。 WSI表现出一种自然的层次结构——切片、区域和幻灯片——具有不同的语义关联。 虽然一些方法试图利用这种层次结构以改善表示，但它们主要依赖欧几里得嵌入，这难以完全捕捉语义层次结构。 为了解决这一限制，我们提出了HyperPath，这是一种新方法，通过整合文本描述的知识来指导WSI语义层次结构在双曲空间中的建模，从而提高WSI分类效果。 我们的方法将病理学视觉-语言基础模型提取的视觉和文本特征适应到双曲空间。 我们设计了一个角度模态对齐损失，以确保跨模态对齐的鲁棒性，同时一个语义层次一致性损失通过蕴含和矛盾关系进一步优化特征层次结构，从而增强语义连贯性。 分类是通过测地线距离进行的，该距离衡量双曲语义层次结构中实体之间的相似性。 这消除了对线性分类器的需求，并实现了面向几何的WSI分析方法。 大量实验表明，与现有方法相比，我们的方法在各项任务中都取得了优越的性能，突显了双曲嵌入在WSI分析中的潜力。 显示英文摘要

摘要： 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.