QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918366016438272 |
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| author | Wang, Rundong Ba, Wei Zhou, Ying Li, Yingtai Liu, Bowen Wang, Baizhi Wang, Yuhao Yang, Zhidong Zhang, Kun Yan, Rui Zhou, S. Kevin |
| author_facet | Wang, Rundong Ba, Wei Zhou, Ying Li, Yingtai Liu, Bowen Wang, Baizhi Wang, Yuhao Yang, Zhidong Zhang, Kun Yan, Rui Zhou, S. Kevin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01647 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image Wang, Rundong Ba, Wei Zhou, Ying Li, Yingtai Liu, Bowen Wang, Baizhi Wang, Yuhao Yang, Zhidong Zhang, Kun Yan, Rui Zhou, S. Kevin Computer Vision and Pattern Recognition 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. |
| title | QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.01647 |