mlsanityData to 7 Oct 2026

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

arXiv 2603.01647 ↗First posted 2 Mar 2026cs.CVcited by 3

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 21 of its 31 references that are in the map. First posted on arXiv 2 Mar 2026.

Builds on

21 of 31 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025PathAgent: Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning21 Nov
  2. 2025SurvAgent: Hierarchical CoT-Enhanced Case Banking and Dichotomy-Based Multi-Agent System for Multimodal Survival Prediction20 Nov
  3. 2025An Agentic System for Rare Disease Diagnosis with Traceable Reasoning25 Jun
  4. 2025PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology13 Feb
  5. 2024Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis29 Jan

Other references · 16

  1. 2025Qwen3 Technical Reportcited by 8,195
  2. 2025Qwen3-VL Technical Reportcited by 2,534
  3. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  4. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 167
  5. 2024Multimodal Whole Slide Foundation Model for Pathologycited by 156
  6. 2024PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathologycited by 80
  7. 2023Benchmarking large language models for biomedical natural language processing applications and recommendationscited by 78
  8. 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 70
  9. 2024SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understandingcited by 54
  10. 2024HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interactioncited by 43
  11. 2025Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasonercited by 38
  12. 2024WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answeringcited by 34
4 more
  1. 2023WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Imagescited by 31
  2. 2025CPathAgent: An Agent-based Foundation Model for Interpretable High-Resolution Pathology Image Analysis Mimicking Pathologists' Diagnostic Logiccited by 27
  3. 2023What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioningcited by 8
  4. 2025Navigating Gigapixel Pathology Images with Large Multimodal Modelscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026PathView-Bench: Can Multimodal Large Language Models Achieve Fine-grained Multiscale Understanding of Pathology Images?cited by 2
  2. 2026Democratizing and accelerating AI-driven pathology research through agentic intelligencecited by 1
  3. 2026Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025WSI-Agents: A Collaborative Multi-Agent System for Multi-Modal Whole Slide Image Analysis
  2. 2025Pathology-CoT: Learning Visual Chain-of-Thought Agent from Expert Whole Slide Image Diagnosis Behavior
  3. 2026Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning
  4. 2026AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning
  5. 2026Decomposing Whole Slide Image Report Generation with Graph-Constrained Multiple Instance Learning Workflows
  6. 2025PolyPath: Adapting a Large Multimodal Model for Multi-slide Pathology Report Generation
  7. 2026Toward Auditable Neuro-Symbolic Reasoning in Pathology: SQL as an Explicit Trace of Evidence
  8. 2025Beyond Pixel Simulation: Pathology Image Generation via Diagnostic Semantic Tokens and Prototype Control