# PathMR: Multimodal Visual Reasoning for Interpretable Pathology Diagnosis

arXiv: https://arxiv.org/abs/2508.20851 · HTML: https://mlsanity.com/paper/2508.20851/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 3 of 3)

- [A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model](https://mlsanity.com/paper/2407.15362/) (2024)
- [GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing](https://mlsanity.com/paper/2501.13925/) (2025)
- [PRIMA: Multi-Image Vision-Language Models for Reasoning Segmentation](https://mlsanity.com/paper/2412.15209/) (2024)

## Built on by (most cited first, 2 of 2)

- [Toward Auditable Neuro-Symbolic Reasoning in Pathology: SQL as an Explicit Trace of Evidence](https://mlsanity.com/paper/2601.01875/) (2026)
- [SIRA: Reasoning-Aware Surgical Instrument Segmentation via Query-Anchored Alignment](https://mlsanity.com/paper/2609.21402/) (2026)

## Abstract (arXiv)

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.