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

TL;DR

QCAgent is a novel agentic framework that enables controllable, evidence-grounded pathology report generation from whole slide images by incorporating user-defined diagnostic constraints and iterative region re-identification.

Contribution

It introduces a critique-guided, iterative process for pathology report generation that allows explicit control over diagnostic details and evidence verification.

Findings

Enables high-coverage, clinically meaningful report generation

Incorporates user-defined diagnostic constraints effectively

Improves grounding of reports in localized visual evidence

Abstract

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.