RadGenome-Chest CT：用于胸部CT分析的基于视觉基础的数据集
RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis
AI总结：
本文介绍RadGenome-Chest CT数据集，基于CT-RATE利用分割和大语言模型扩展，提供器官级分割掩码、基础报告和VQA对，推进多模态医学基础模型发展。
摘要：
开发通才基础模型最近在医学人工智能（AI4Medicine）领域引起了研究者的极大关注。开发这些模型的一个关键见解是它们依赖数据集规模的扩展，这强调了开发开源医学图像数据集的需求，这些数据集需包含跨各种成像模态的多样化监督信号。在本文中，我们介绍了RadGenome-Chest CT，这是一个基于CT-RATE构建的全面、大规模、区域引导的3D胸部CT解释数据集。具体而言，我们利用最新强大的通用分割模型和大语言模型，从以下几个方面扩展了原始数据集（包含来自20,000名患者的超过25,692个非对比3D胸部CT体积和报告）：（i）涵盖197个类别的器官级分割掩码，为解释提供中间推理的视觉线索；（ii）665K多粒度基础报告，其中报告的每个句子都以分割掩码的形式链接到CT体积的相应解剖区域；（iii）1.3M基础VQA对，其中问题和答案都与参考分割掩码链接，使模型能够将视觉证据与文本解释相关联。验证集中的所有基础报告和VQA对都经过了人工验证，以确保数据集质量。我们相信，RadGenome-Chest CT可以通过训练基于给定分割区域生成文本，显著推进多模态医学基础模型的发展，这是以前相关数据集无法实现的。我们将发布所有分割掩码、基础报告和VQA对，以促进该领域的进一步研究和开发。
英文摘要：
Developing generalist foundation model has recently attracted tremendous attention among researchers in the field of AI for Medicine (AI4Medicine). A pivotal insight in developing these models is their reliance on dataset scaling, which emphasizes the requirements on developing open-source medical image datasets that incorporate diverse supervision signals across various imaging modalities. In this paper, we introduce RadGenome-Chest CT, a comprehensive, large-scale, region-guided 3D chest CT interpretation dataset based on CT-RATE. Specifically, we leverage the latest powerful universal segmentation and large language models, to extend the original datasets (over 25,692 non-contrast 3D chest CT volume and reports from 20,000 patients) from the following aspects: (i) organ-level segmentation masks covering 197 categories, which provide intermediate reasoning visual clues for interpretation; (ii) 665 K multi-granularity grounded reports, where each sentence of the report is linked to the corresponding anatomical region of CT volume in the form of a segmentation mask; (iii) 1.3 M grounded VQA pairs, where questions and answers are all linked with reference segmentation masks, enabling models to associate visual evidence with textual explanations. All grounded reports and VQA pairs in the validation set have gone through manual verification to ensure dataset quality. We believe that RadGenome-Chest CT can significantly advance the development of multimodal medical foundation models, by training to generate texts based on given segmentation regions, which is unattainable with previous relevant datasets. We will release all segmentation masks, grounded reports, and VQA pairs to facilitate further research and development in this field.
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。
