PathVLM-R1: A Reinforcement Learning-Driven Reasoning Model for Pathology Visual-Language Tasks

2025-04-12

AI Summary

To address the weak reasoning capability, lack of controllability, and poor interpretability of vision-language models (VLMs) in pathological image diagnosis, this paper proposes PathVLM-R1—the first pathology-domain-specific VLM with strong reasoning and inherent interpretability. Methodologically, we introduce a novel dual-reward-driven Group Relative Policy Optimization (GRPO) framework that jointly optimizes logical coherence and answer accuracy; to our knowledge, this is the first work to deeply integrate reinforcement learning into the end-to-end training pipeline of pathological VLMs. Built upon Qwen2.5-VL-7B-Instruct, PathVLM-R1 incorporates pathology-specific supervised fine-tuning (SFT), cross-modal process reward modeling, and outcome reward modeling. Experiments demonstrate a 14% absolute accuracy gain on pathological question answering—outperforming the significantly larger Qwen2.5-VL-32B (4.6× parameters). Moreover, cross-modal transfer performance improves by an average of 17.3%.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• 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

The diagnosis of pathological images is often limited by expert availability and regional disparities, highlighting the importance of automated diagnosis using Vision-Language Models (VLMs). Traditional multimodal models typically emphasize outcomes over the reasoning process, compromising the reliability of clinical decisions. To address the weak reasoning abilities and lack of supervised processes in pathological VLMs, we have innovatively proposed PathVLM-R1, a visual language model designed specifically for pathological images. We have based our model on Qwen2.5-VL-7B-Instruct and enhanced its performance for pathological tasks through meticulously designed post-training strategies. Firstly, we conduct supervised fine-tuning guided by pathological data to imbue the model with foundational pathological knowledge, forming a new pathological base model. Subsequently, we introduce Group Relative Policy Optimization (GRPO) and propose a dual reward-driven reinforcement learning optimization, ensuring strict constraint on logical supervision of the reasoning process and accuracy of results via cross-modal process reward and outcome accuracy reward. In the pathological image question-answering tasks, the testing results of PathVLM-R1 demonstrate a 14% improvement in accuracy compared to baseline methods, and it demonstrated superior performance compared to the Qwen2.5-VL-32B version despite having a significantly smaller parameter size. Furthermore, in out-domain data evaluation involving four medical imaging modalities: Computed Tomography (CT), dermoscopy, fundus photography, and Optical Coherence Tomography (OCT) images: PathVLM-R1's transfer performance improved by an average of 17.3% compared to traditional SFT methods. These results clearly indicate that PathVLM-R1 not only enhances accuracy but also possesses broad applicability and expansion potential.

Problem

• Enhances reasoning in pathology VLMs for reliable diagnoses
• Addresses weak reasoning and lack of supervision in VLMs
• Improves accuracy and transferability in medical image analysis

Innovation

• Reinforcement learning-driven reasoning model for pathology
• Dual reward-driven optimization for logical supervision
• Supervised fine-tuning with pathological data enhancement