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

• URL: http://arxiv.org/abs/2603.01647v1
• Date: Mon, 02 Mar 2026 09:27:31 GMT
• Title: QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image
• Abstract summary: QCAgent is an agentic framework for quality-controllable pathology report generation from whole-slide image (WSI)<n>It incorporates a customized critique mechanism guided by a user-defined checklist specifying required diagnostic details and constraints.<n>It re-identifies informative regions in the WSI based on the critique feedback and text-patch semantic retrieval.
• Score: 44.81983055985989
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation [66.62662330558906]
  CORAL is a multimodal framework that integrates spatial grounding and concept-level supervision into a unified reasoning process.<n>CORAL employs a prompt-driven medical segmentation model to localize lesions and predicts multi-class clinical attributes through a Concept Bottleneck module.<n>Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs.
  arXiv  Detail & Related papers  (2026-09-14T10:21:23Z)
• Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology [54.42160154368719]
  Whole-slide images (WSI) have enabled the digital transformation of pathology.<n> multimodal models that jointly analyze histopathology images and pathology reports have shown promising potential for automated pathology report generation and AI-assisted diagnosis.<n>Here, we introduce TUM-Uteria, a uterine pathology dataset comprising WSIs paired with diagnostic pathology reports at both the case and slide levels.
  arXiv  Detail & Related papers  (2026-07-04T20:34:59Z)
• Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents [4.316569498378906]
  We present the Insights Generator (IG), a multi-agent system that answers diagnostic questions by proposing and testing hypotheses across the trace corpus.<n>Human experts using IG reports improve scaffold performance by 30.4pp over the unmodified baseline scaffold.<n>IG's scout-investigator architecture produces findings comparable in detection coverage to competing approaches.
  arXiv  Detail & Related papers  (2026-05-20T16:13:53Z)
• Layout-Guided Controllable Pathology Image Generation with In-Context Diffusion Transformers [57.54843029965778]
  Controllable pathology image synthesis requires reliable regulation of spatial layout, tissue morphology, and semantic detail.<n>We propose In-Context Diffusion Transformer (IC-DiT), a layout-aware generative model that incorporates spatial layouts, textual descriptions, and visual embeddings into a unified diffusion transformer.<n>IC-DiT achieves higher fidelity, stronger spatial controllability, and better diagnostic consistency than existing methods.
  arXiv  Detail & Related papers  (2026-03-11T06:14:11Z)
• MedCollab: Causal-Driven Multi-Agent Collaboration for Full-Cycle Clinical Diagnosis via IBIS-Structured Argumentation [6.334763475104128]
  We present MedCollab, a novel multi-agent framework that emulates the hierarchical consultation workflow of modern hospitals.<n>The framework incorporates a dynamic specialist recruitment mechanism that adaptively assembles clinical and examination agents according to patient-specific symptoms and examination results.
  arXiv  Detail & Related papers  (2026-03-01T14:25:58Z)
• AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
  We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
  arXiv  Detail & Related papers  (2026-01-23T11:59:13Z)
• CTIS-QA: Clinical Template-Informed Slide-level Question Answering for Pathology [14.647193383163284]
  We introduce a clinical diagnosis template-based pipeline to systematically collect and structure pathological information.<n>CTIS-QA is a Slide-level Question Answering model, featuring a dual-stream architecture that mimics pathologists' diagnostic approach.
  arXiv  Detail & Related papers  (2026-01-05T03:54:02Z)
• EHRSummarizer: A Privacy-Aware, FHIR-Native Architecture for Structured Clinical Summarization of Electronic Health Records [0.0]
  EHRSummarizer produces structured summaries to support structured chart review.<n>System can be configured for data minimization, stateless processing, and flexible deployment.
  arXiv  Detail & Related papers  (2026-01-04T21:10:42Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging [67.74482877175797]
  MIRNet is a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning.<n>We introduce TongueAtlas-4K, a benchmark comprising 4,000 images annotated with 22 diagnostic labels.
  arXiv  Detail & Related papers  (2025-11-13T06:30:41Z)
• CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation [62.0150409256153]
  We propose CXRAgent, a director-orchestrated, multi-stage agent for CXR interpretation.<n>The agent strategically orchestrates a set of CXR-analysis tools, with outputs normalized and verified by the Evidence-driven Validator.<n>Experiments on various CXR interpretation tasks show that CXRAgent delivers strong performance, providing visual evidence and generalizes well to clinical tasks of different complexity.
  arXiv  Detail & Related papers  (2025-10-24T10:31:30Z)
• Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation [32.410641778559544]
  ICARE (Interpretable and Clinically-grounded Agent-based Report Evaluation) is an interpretable evaluation framework.<n>Two agents, each with either the ground-truth or generated report, generate clinically meaningful questions and quiz each other.<n>By linking scores to question-answer pairs, ICARE enables transparent, and interpretable assessment.
  arXiv  Detail & Related papers  (2025-08-04T18:28:03Z)
• RadFabric: Agentic AI System with Reasoning Capability for Radiology [61.25593938175618]
  RadFabric is a multi agent, multimodal reasoning framework that unifies visual and textual analysis for comprehensive CXR interpretation.<n>System employs specialized CXR agents for pathology detection, an Anatomical Interpretation Agent to map visual findings to precise anatomical structures, and a Reasoning Agent powered by large multimodal reasoning models to synthesize visual, anatomical, and clinical data into transparent and evidence based diagnoses.
  arXiv  Detail & Related papers  (2025-06-17T03:10:33Z)
• CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models [1.7042756021131187]
  This paper presents an automated radiology report generation framework that combines Concept Bottleneck Models (CBMs) with a Multi-Agent Retrieval-Augmented Generation (RAG) system.<n>CBMs map chest X-ray features to human-understandable clinical concepts, enabling transparent disease classification.<n>RAG system integrates multi-agent collaboration and external knowledge to produce contextually rich, evidence-based reports.
  arXiv  Detail & Related papers  (2025-04-29T16:14:55Z)
• Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm [36.60917255464867]
  We propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novel response-proficiency-response paradigm inspired by encoder-decoder models. We show that ID-CDF can effectively address the problems without loss of diagnosis preciseness.
  arXiv  Detail & Related papers  (2023-09-01T07:18:02Z)
• PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation [7.508437260320598]
  We propose diagnosis-driven prompts for medical report generation (PromptMRG) PromptMRG is based on encoder-decoder architecture with an extra disease classification branch. Cross-modal feature enhancement retrieves similar reports from the database to assist the diagnosis of a query image.
  arXiv  Detail & Related papers  (2023-08-24T07:10:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.