让病理报告生成可控制、有依据，精准匹配诊断需求。
QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image
- 引入用户自定义清单，驱动报告逐轮优化
- 根据反馈重定位关键区域，提升报告覆盖率与准确性
- 适合临床医生用于定制化、可验证的病理报告生成
从全切片图像（WSI）生成病理报告的现有方法虽能提供整体诊断描述，却难以将细节性结论与局部视觉证据对齐，且缺乏对报告内容的可控性与验证机制。受病理学家分步阅片流程启发，我们提出QCAgent——一种质量可控的病理报告生成智能体框架。该框架核心创新包括：(i) 基于用户定义检查清单的定制化批评机制，明确要求诊断细节与约束；(ii) 根据批评反馈和图文语义检索，迭代重识别图像中有信息量的区域，持续丰富并校正报告内容。实验表明，通过显式提示定义报告需求、具备约束感知能力，并经由基于证据的修正过程，QCAgent可生成临床有意义、高覆盖度的病理报告。
原文摘要 · Abstract (English)
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.
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