CT-Agent：用于三维CT放射学问答的多模态大语言模型智能体
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering
AI总结：
针对CT放射学问答中解剖复杂和跨切片空间关系难以捕捉的问题，提出多模态智能体框架CT-Agent，采用解剖独立工具和全局-局部令牌压缩策略，在两个三维胸部CT数据集上验证了优越性能。
摘要：
计算机断层扫描（CT）产生的三维体积医学数据可视为数百张横断面图像（即切片），为诊断提供详细的解剖信息。对于放射科医生而言，撰写CT放射学报告既耗时又容易出错。一个能够回答放射科医生关于CT扫描中某些解剖区域的问题，甚至自动生成放射学报告的视觉问答（VQA）系统亟待开发。然而，现有的VQA系统无法充分处理CT放射学问答（CTQA）任务，原因在于：（1）解剖复杂性使得CT图像难以理解；（2）跨越数百张切片的空间关系难以捕捉。为解决这些问题，本文提出了CT-Agent，一种用于CTQA的多模态智能体框架。CT-Agent采用解剖学独立的工具来分解解剖复杂性；此外，它通过全局-局部令牌压缩策略高效捕捉跨切片空间关系。在两个三维胸部CT数据集CT-RATE和RadGenome-ChestCT上的实验结果验证了CT-Agent的优越性能。
英文摘要：
Computed Tomography (CT) scan, which produces 3D volumetric medical data that can be viewed as hundreds of cross-sectional images (a.k.a. slices), provides detailed anatomical information for diagnosis. For radiologists, creating CT radiology reports is time-consuming and error-prone. A visual question answering (VQA) system that can answer radiologists' questions about some anatomical regions on the CT scan and even automatically generate a radiology report is urgently needed. However, existing VQA systems cannot adequately handle the CT radiology question answering (CTQA) task for: (1) anatomic complexity makes CT images difficult to understand; (2) spatial relationship across hundreds slices is difficult to capture. To address these issues, this paper proposes CT-Agent, a multimodal agentic framework for CTQA. CT-Agent adopts anatomically independent tools to break down the anatomic complexity; furthermore, it efficiently captures the across-slice spatial relationship with a global-local token compression strategy. Experimental results on two 3D chest CT datasets, CT-RATE and RadGenome-ChestCT, verify the superior performance of CT-Agent.
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。
