开发一个大规模的基于视觉语言的数据集,用于胸部CT分析
Xiaoman Zhang1,2, Chaoyi Wu1,2, Ziheng Zhao1,2
1Shanghai Jiao Tong University, Shanghai, China.
Scientific data
|October 10, 2025
概括
这项研究介绍了RadGenome-Chest CT,这是一套用于医学人工智能的大型数据集. 它具有区域引导的3D胸部CT解释,以推进多式模式的基础模型.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 基金会模型 基金会模型
背景情况:
- 在AI for Medicine中开发通用基础模型需要多样化的开源医疗图像数据集.
- 现有的数据集往往缺乏跨各种成像模式的全面监督信号.
研究的目的:
- 介绍RadGenome-Chest CT,一个新的,大规模的,区域导向的3D胸部CT解释数据集.
- 通过提供适合多式联络基础模型开发的数据集来增强医学AI.
主要方法:
- 利用通用细分模型和大型语言模型来扩展现有数据集.
- 包含了197个类别的器官级别细分口罩.
- 产生了665K个多细分性接地报告和1.2M个接地视觉问答 (VQA) 对.
主要成果:
- 创建了一个数据集,包含广泛的区域指导注释,用于3D胸部CT解释.
- 启用了通过分段口罩将文本报告和VQA对与特定的解剖区域联系起来.
- 基于细分区域的文本生成方便培训.
结论:
- 雷德基因组-胸部CT显著推进了多式联络医疗基础模型的开发.
- 数据集的独特特性使得以前的数据集无法实现的培训能力成为可能.
- 准备加速人工智能驱动的医学图像解释的进展.
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