Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner

TL;DR

Patho-R1 is a multimodal reinforcement learning-based pathology reasoning system that leverages high-quality datasets and a three-stage training pipeline to improve diagnostic accuracy and reasoning in pathology tasks.

Contribution

The paper introduces Patho-R1, a novel multimodal RL-based pathology reasoner trained on high-quality datasets with a three-stage pipeline, enhancing reasoning and diagnostic performance.

Findings

Patho-R1 achieves robust performance on pathology tasks.

Patho-CLIP aligns well with pathology datasets.

Reinforcement learning improves reasoning quality.

Abstract

Recent advances in vision language models (VLMs) have enabled broad progress in the general medical field. However, pathology still remains a more challenging subdomain, with current pathology specific VLMs exhibiting limitations in both diagnostic accuracy and reasoning plausibility. Such shortcomings are largely attributable to the nature of current pathology datasets, which are primarily composed of image description pairs that lack the depth and structured diagnostic paradigms employed by real world pathologists. In this study, we leverage pathology textbooks and real world pathology experts to construct high-quality, reasoning-oriented datasets. Building on this, we introduce Patho-R1, a multimodal RL-based pathology Reasoner, trained through a three-stage pipeline: (1) continued pretraining on 3.5 million image-text pairs for knowledge infusion; (2) supervised fine-tuning on 500k high-quality Chain-of-Thought samples for reasoning incentivizing; (3) reinforcement learning using Group Relative Policy Optimization and Decoupled Clip and Dynamic sAmpling Policy Optimization strategies for multimodal reasoning quality refinement. To further assess the alignment quality of our dataset, we propose Patho-CLIP, trained on the same figure-caption corpus used for continued pretraining. Comprehensive experimental results demonstrate that both Patho-CLIP and Patho-R1 achieve robust performance across a wide range of pathology-related tasks, including zero-shot classification, cross-modal retrieval, Visual Question Answering, and Multiple Choice Question. Our project is available at the Patho-R1 repository: https://github.com/Wenchuan-Zhang/Patho-R1.