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ReinPath: A Multimodal Reinforcement Learning Approach for Pathology

Kangcheng Zhou, Jun Jiang, Qing Zhang, Shuang Zheng, Qingli Li, Shugong Xu

TL;DR

ReinPath tackles the need for interpretable multimodal pathology models by introducing ReinPathVQA, a high-quality pathology VQA dataset with explicit reasoning annotations, and a multimodal LLM framework that leverages semantic rewards within a Group Relative Policy Optimization regime. The approach fuses visual features from two pathology foundation models and trains through a three-stage process (feature alignment, cold-start instruction tuning, and reinforcement learning) to generate contextually accurate and interpretable descriptions. Empirical results show ReinPath achieves state-of-the-art or strong performance on multiple VQA benchmarks with limited training data, and also performs competitively on zero-shot classification, highlighting data efficiency and explainability. The work advances practical pathology AI by improving reasoning transparency and robustness, potentially enhancing clinical reliability in diagnostic decision support.

Abstract

Interpretability is significant in computational pathology, leading to the development of multimodal information integration from histopathological image and corresponding text data.However, existing multimodal methods have limited interpretability due to the lack of high-quality dataset that support explicit reasoning and inference and simple reasoning process.To address the above problems, we introduce a novel multimodal pathology large language model with strong reasoning capabilities.To improve the generation of accurate and contextually relevant textual descriptions, we design a semantic reward strategy integrated with group relative policy optimization.We construct a high-quality pathology visual question answering (VQA) dataset, specifically designed to support complex reasoning tasks.Comprehensive experiments conducted on this dataset demonstrate that our method outperforms state-of-the-art methods, even when trained with only 20% of the data.Our method also achieves comparable performance on downstream zero-shot image classification task compared with CLIP.

ReinPath: A Multimodal Reinforcement Learning Approach for Pathology

TL;DR

ReinPath tackles the need for interpretable multimodal pathology models by introducing ReinPathVQA, a high-quality pathology VQA dataset with explicit reasoning annotations, and a multimodal LLM framework that leverages semantic rewards within a Group Relative Policy Optimization regime. The approach fuses visual features from two pathology foundation models and trains through a three-stage process (feature alignment, cold-start instruction tuning, and reinforcement learning) to generate contextually accurate and interpretable descriptions. Empirical results show ReinPath achieves state-of-the-art or strong performance on multiple VQA benchmarks with limited training data, and also performs competitively on zero-shot classification, highlighting data efficiency and explainability. The work advances practical pathology AI by improving reasoning transparency and robustness, potentially enhancing clinical reliability in diagnostic decision support.

Abstract

Interpretability is significant in computational pathology, leading to the development of multimodal information integration from histopathological image and corresponding text data.However, existing multimodal methods have limited interpretability due to the lack of high-quality dataset that support explicit reasoning and inference and simple reasoning process.To address the above problems, we introduce a novel multimodal pathology large language model with strong reasoning capabilities.To improve the generation of accurate and contextually relevant textual descriptions, we design a semantic reward strategy integrated with group relative policy optimization.We construct a high-quality pathology visual question answering (VQA) dataset, specifically designed to support complex reasoning tasks.Comprehensive experiments conducted on this dataset demonstrate that our method outperforms state-of-the-art methods, even when trained with only 20% of the data.Our method also achieves comparable performance on downstream zero-shot image classification task compared with CLIP.
Paper Structure (10 sections, 8 equations, 4 figures, 4 tables)

This paper contains 10 sections, 8 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Dataset Construction Pipeline
  4. Multimodal Reinforcement Learning Framework
  5. Training Strategy
  6. Experiments
  7. Implementation Details
  8. Experiment Results
  9. Ablation Study
  10. Conclusion

Figures (4)

  • Figure 1: Performance of different fine-tuning samples and model sizes on the PathMMU dataset in VQA task.
  • Figure 2: ReinPathVQA dataset construction pipeline (a) and the dataset's distribution (b).
  • Figure 3: Overview of our ReinPath (a) with three training stages (b): Feature Alignment, Cold Start and GRPO.
  • Figure 4: Ablation experiments with encoder (a), training strategy (b) and SFT data size (c). Comparison of model response length and accuracy on two VQA datasets (d).