PathMR: Multimodal Visual Reasoning for Interpretable Pathology Diagnosis
This work addresses the need for interpretable AI in pathology diagnosis, which is crucial for clinical adoption, though it appears incremental as it builds on existing multimodal visual reasoning architectures.
The paper tackled the problem of opaque model decisions in automated pathological diagnosis by proposing PathMR, a multimodal visual reasoning framework that generates diagnostic explanations and predicts cell distribution patterns, achieving state-of-the-art performance in text generation quality, segmentation accuracy, and cross-modal alignment on benchmark datasets.
Deep learning based automated pathological diagnosis has markedly improved diagnostic efficiency and reduced variability between observers, yet its clinical adoption remains limited by opaque model decisions and a lack of traceable rationale. To address this, recent multimodal visual reasoning architectures provide a unified framework that generates segmentation masks at the pixel level alongside semantically aligned textual explanations. By localizing lesion regions and producing expert style diagnostic narratives, these models deliver the transparent and interpretable insights necessary for dependable AI assisted pathology. Building on these advancements, we propose PathMR, a cell-level Multimodal visual Reasoning framework for Pathological image analysis. Given a pathological image and a textual query, PathMR generates expert-level diagnostic explanations while simultaneously predicting cell distribution patterns. To benchmark its performance, we evaluated our approach on the publicly available PathGen dataset as well as on our newly developed GADVR dataset. Extensive experiments on these two datasets demonstrate that PathMR consistently outperforms state-of-the-art visual reasoning methods in text generation quality, segmentation accuracy, and cross-modal alignment. These results highlight the potential of PathMR for improving interpretability in AI-driven pathological diagnosis. The code will be publicly available in https://github.com/zhangye-zoe/PathMR.