PathoHR: Hierarchical Reasoning for Vision-Language Models in Pathology
This work addresses the problem of improving automated tumor diagnosis for clinical applications by enhancing vision-language models' ability to interpret pathological images and reports, though it is incremental as it builds on existing multimodal methods.
The authors tackled the challenge of vision-language models struggling with hierarchical reasoning in pathology by introducing PathoHR-Bench, a benchmark for evaluating these models, and a training scheme that achieved state-of-the-art performance on this benchmark and six other datasets.
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.