标题： DiagR1：通过强化学习训练的用于消化道病理诊断的视觉-语言模型 显示英文标题

标题： DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis

摘要： 多模态大模型在自动化病理图像分析方面显示出巨大的潜力。 然而，当前用于胃肠道病理的多模态模型受到数据质量和推理透明度的双重限制：公共数据集中普遍存在的噪声和不完整的注释会导致视觉语言模型在生成诊断文本时出现事实性幻觉，而缺乏明确的中间推理链使得输出难以审核，因此在临床实践中可信度较低。 为了解决这些问题，我们构建了一个包含显微描述和诊断结论的大规模胃肠道病理数据集，并提出了一种提示论证策略，该策略结合了病变分类和解剖部位信息。 这种设计引导模型更好地捕捉图像特定特征并保持生成过程中的语义一致性。 此外，我们采用了一个后训练流程，结合监督微调与组相对策略优化（GRPO）来提高推理质量和输出结构。 在真实世界病理报告生成任务上的实验结果表明，我们的方法在生成质量、结构完整性和临床相关性方面显著优于最先进的开源和专有基线模型。 我们的解决方案在临床相关性上比最先进的模型高18.7%，结构完整性提高了32.4%，诊断错误减少了41.2%，与现有解决方案相比，表现出更高的准确性和临床实用性。 显示英文摘要

摘要： Multimodal large models have shown great potential in automating pathology image analysis. However, current multimodal models for gastrointestinal pathology are constrained by both data quality and reasoning transparency: pervasive noise and incomplete annotations in public datasets predispose vision language models to factual hallucinations when generating diagnostic text, while the absence of explicit intermediate reasoning chains renders the outputs difficult to audit and thus less trustworthy in clinical practice. To address these issues, we construct a large scale gastrointestinal pathology dataset containing both microscopic descriptions and diagnostic conclusions, and propose a prompt argumentation strategy that incorporates lesion classification and anatomical site information. This design guides the model to better capture image specific features and maintain semantic consistency in generation. Furthermore, we employ a post training pipeline that combines supervised fine tuning with Group Relative Policy Optimization (GRPO) to improve reasoning quality and output structure. Experimental results on real world pathology report generation tasks demonstrate that our approach significantly outperforms state of the art open source and proprietary baselines in terms of generation quality, structural completeness, and clinical relevance. Our solution outperforms state of the art models with 18.7% higher clinical relevance, 32.4% improved structural completeness, and 41.2% fewer diagnostic errors, demonstrating superior accuracy and clinical utility compared to existing solutions.