PathoHR: Hierarchical Reasoning for Vision-Language Models in Pathology

2025-09-07

AI Summary

Pathological images exhibit high structural similarity and subtle morphological variations, posing significant challenges for automated tumor diagnosis; existing vision-language models struggle to capture hierarchical cross-modal semantics and compositional reasoning relationships. To address this, we propose PathoHR-Bench—the first hierarchical reasoning benchmark tailored for pathology—and introduce a pathology-specific multimodal contrastive learning framework. Our approach integrates hierarchical semantic alignment, compositional reasoning mechanisms, and generative data augmentation with perturbation strategies. The framework substantially enhances model capability in representing fine-grained pathological features and modeling complex cross-modal relationships. It achieves state-of-the-art performance on PathoHR-Bench and six mainstream pathological datasets. This work establishes a new paradigm for improving interpretability and clinical utility of vision-language models in diagnostic pathology.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Accurate analysis of pathological images is essential for automated tumor diagnosis but remains challenging due to high structural similarity and subtle morphological variations in tissue images. Current vision-language (VL) models often struggle to capture the complex reasoning required for interpreting structured pathological reports. To address these limitations, we propose PathoHR-Bench, a novel benchmark designed to evaluate VL models' abilities in hierarchical semantic understanding and compositional reasoning within the pathology domain. Results of this benchmark reveal that existing VL models fail to effectively model intricate cross-modal relationships, hence limiting their applicability in clinical setting. To overcome this, we further introduce a pathology-specific VL training scheme that generates enhanced and perturbed samples for multimodal contrastive learning. Experimental evaluations demonstrate that our approach achieves state-of-the-art performance on PathoHR-Bench and six additional pathology datasets, highlighting its effectiveness in fine-grained pathology representation.

Problem

• Automated tumor diagnosis struggles with subtle tissue variations
• VL models fail to capture hierarchical pathological reasoning
• Current methods cannot model complex cross-modal clinical relationships

Innovation

• Hierarchical reasoning benchmark for pathology VL models
• Multimodal contrastive learning with enhanced perturbed samples
• Pathology-specific training scheme for fine-grained representation