arXiv 2603.016472026-03-02cs.CV

QCAgent：一种用于从全切片图像生成质量可控病理科报告的代理框架

QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image

School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China, Hefei, Anhui, 230026, P.R. China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, 215123, P.R. China Chinese PLA General Hospital Ninth Medical Center, China Hong Kong University of Science

Rundong Wang, Wei Ba, Ying Zhou, Yingtai Li, Bowen Liu, Baizhi Wang, Yuhao Wang, Zhidong Yang, Kun Zhang, Rui Yan, S. Kevin Zhou

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AI总结：

QCAgent通过引入定制批评机制和证据驱动的细化流程，实现从全切片图像生成质量可控、具有临床意义的病理科报告。

摘要：

最近用于从全切片图像（WSI）生成病理科报告的方法能够生成滑片级别的诊断描述，但无法将细粒度的陈述 grounded 在局部视觉证据中。此外，它们缺乏对要包含的诊断细节和如何验证它们的控制。受新兴代理分析范式和病理科医生的诊断工作流程启发，后者会选择性地检查多个视野，我们提出了QCAgent，一种用于质量可控的WSI报告生成的代理框架。该框架的核心创新如下：（i）它结合了一个由用户定义的检查表引导的定制批评机制，指定所需诊断细节和约束；（ii）它基于批评反馈和文本-补丁语义检索重新识别信息区域，这一过程通过迭代丰富和协调报告来实现。实验表明，通过使报告要求显式地定义、约束-aware 并通过证据-grounded 的细化进行验证，QCAgent能够从WSI中生成具有临床意义和高覆盖度的病理科报告。

英文摘要：

Recent methods for pathology report generation from whole-slide image (WSI) are capable of producing slide-level diagnostic descriptions but fail to ground fine-grained statements in localized visual evidence. Furthermore, they lack control over which diagnostic details to include and how to verify them. Inspired by emerging agentic analysis paradigms and the diagnostic workflow of pathologists,who selectively examine multiple fields of view, we propose QCAgent, an agentic framework for quality-controllable WSI report generation. The core innovations of this framework are as follows: (i) it incorporates a customized critique mechanism guided by a user-defined checklist specifying required diagnostic details and constraints; (ii) it re-identifies informative regions in the WSI based on the critique feedback and text-patch semantic retrieval, a process that iteratively enriches and reconciles the report. Experiments demonstrate that by making report requirements explicitly prompt-defined, constraint-aware, and verifiable through evidence-grounded refinement, QCAgent enables controllable generation of clinically meaningful and high-coverage pathology reports from WSI.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。