arXiv 2512.235452025-12-29cs.CVcs.AI

PathFound: 一种促进证据寻求病理诊断的代理多模态模型

PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis

Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学庆元研究院）；Shanghai Innovation Institute（上海创新研究院）；Department of Pathology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学学院病理科）；Intelligent Pathology Institute, Division of Life Sciences and Medicine（生命科学与医学学院智能病理研究所）；Department of Pathology, Fudan University Shanghai Cancer Center（复旦大学上海癌症中心病理科）；Department of Oncology, Shanghai Medical College, Fudan University（复旦大学上海医学院肿瘤科）；Institute of Pathology, Fudan University（复旦大学病理研究所）；Department of Pathology, The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院病理科）

Shengyi Hua, Jianfeng Wu, Tianle Shen, Kangzhe Hu, Zhongzhen Huang, Shujuan Ni, Zhihong Zhang, Yuan Li, Zhe Wang, Xiaofan Zhang

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AI总结：

PathFound是一种通过主动信息获取和诊断完善来提升病理诊断准确性的代理多模态模型，其在多种临床场景中表现出卓越的诊断性能。

摘要：

最近的病理基础模型在视觉表示学习和多模态交互方面取得了显著进展。然而，大多数模型仍然依赖于静态推断范式，在这种范式下，整个切片图像被一次处理以生成预测，没有在诊断模糊时进行重新评估或有针对性的证据获取。这与临床诊断流程形成对比，后者通过反复的切片观察和进一步的检查请求来完善假设。我们提出了PathFound，一种旨在支持病理诊断中证据寻求推断的代理多模态模型。PathFound整合了病理视觉基础模型、视觉语言模型以及通过强化学习训练的推理模型的力量，通过初始诊断、证据寻求和最终决策阶段逐步推进，以实现主动的信息获取和诊断完善。在多个大型多模态模型中，采用这种策略一致地提高了诊断准确性，表明证据寻求工作流程在计算病理学中的有效性。在这些模型中，PathFound在多种临床场景中实现了最先进的诊断性能，并展示了发现细微特征，如核特征和局部浸润的强潜力。

英文摘要：

Recent pathological foundation models have substantially advanced visual representation learning and multimodal interaction. However, most models still rely on a static inference paradigm in which whole-slide images are processed once to produce predictions, without reassessment or targeted evidence acquisition under ambiguous diagnoses. This contrasts with clinical diagnostic workflows that refine hypotheses through repeated slide observations and further examination requests. We propose PathFound, an agentic multimodal model designed to support evidence-seeking inference in pathological diagnosis. PathFound integrates the power of pathological visual foundation models, vision-language models, and reasoning models trained with reinforcement learning to perform proactive information acquisition and diagnosis refinement by progressing through the initial diagnosis, evidence-seeking, and final decision stages. Across several large multimodal models, adopting this strategy consistently improves diagnostic accuracy, indicating the effectiveness of evidence-seeking workflows in computational pathology. Among these models, PathFound achieves state-of-the-art diagnostic performance across diverse clinical scenarios and demonstrates strong potential to discover subtle details, such as nuclear features and local invasions.

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