INFORM-CT：整合LLM和VLM用于腹部CT的偶发发现管理
INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT
AI总结：
本文提出基于LLM和VLM的计划-执行框架，用于提高腹部CT偶发发现的检测、分类和报告效率与精度，通过自动化流程提升临床应用效果。
摘要：
CT扫描中的偶发发现虽然常为良性，但具有重要临床意义，应按指南报告。传统手动检查由放射科医生进行，耗时且结果不一。本文提出一种新颖框架，利用大语言模型（LLM）和基础视觉-语言模型（VLM）的计划-执行代理方法，提高腹部CT偶发发现的检测、分类和报告效率与精度。基于腹部器官的医学指南，通过计划-执行框架自动管理偶发发现。计划基于LLM生成Python脚本，执行器运行这些脚本，通过VLM、分割模型和图像处理子程序进行必要的检查和检测。通过在三个器官的CT腹部基准数据集上进行实验，证明了该方法的有效性。结果表明，所提框架在准确性和效率上优于现有纯VLM方法。
英文摘要：
Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established guidelines. Traditional manual inspection by radiologists is time-consuming and variable. This paper proposes a novel framework that leverages large language models (LLMs) and foundational vision-language models (VLMs) in a plan-and-execute agentic approach to improve the efficiency and precision of incidental findings detection, classification, and reporting for abdominal CT scans. Given medical guidelines for abdominal organs, the process of managing incidental findings is automated through a planner-executor framework. The planner, based on LLM, generates Python scripts using predefined base functions, while the executor runs these scripts to perform the necessary checks and detections, via VLMs, segmentation models, and image processing subroutines. We demonstrate the effectiveness of our approach through experiments on a CT abdominal benchmark for three organs, in a fully automatic end-to-end manner. Our results show that the proposed framework outperforms existing pure VLM-based approaches in terms of accuracy and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
