ROCOv2:Context版本2中的放射学物体,一个更新的多模式图像数据集
Johannes Rückert1, Louise Bloch1,2,3, Raphael Brüngel1,2,3
1Department of Computer Science, University of Applied Sciences and Arts Dortmund, Dortmund, Germany.
Scientific data
|June 26, 2024
概括
这项研究介绍了ROCOv2,这是一个大型多式联络数据集,包含79,789张带有医学概念和标题的放射图像. 它通过为注释和分类模型提供有价值的训练数据来增强自动化医疗图像分析.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 自动化医疗图像分析需要广泛的,高质量的标记训练数据,这是具有挑战性的生产.
- 现有的数据集可能缺乏高级深度学习模型所需的规模或多式联络注释.
研究的目的:
- 在COntext版本2 (ROCOv2) 中介绍放射学对象,这是一个更新和扩展的医学成像多式联络数据集.
- 促进自动化医疗图像分析系统的开发和评估,特别是用于概念检测和标题预测.
- 为训练图像注释模型,多标签图像分类和预训练医学领域模型提供全面的资源.
主要方法:
- 编译了ROCOv2,通过更新原来的ROCO数据集,从PMC开放访问子集中新增35,705张放射图像.
- 整合相关的医学概念,包括手动策划的成像模式,X射线的解剖学和定向概念.
- 从PMC开放访问子集中提取医学概念和标题,利用统一的医学语言系统 (UMLS).
主要成果:
- ROCOv2包含了79,789张放射图像,以及相关的医学概念和标题.
- 数据集包括对成像方式,解剖学和方向性的增强注释,特别是对于X射线.
- 在ImageCLEF医疗标题2023中,ROCOv2已成功地用于概念检测和标题预测任务.
结论:
- ROCOv2提供了一个有价值的,大规模的多式联络数据集,用于推进自动化医疗图像分析.
- 该数据集支持各种应用,包括图像注释,多标签分类和深度学习模型的预训练.
- ROCOv2适用于评估医学成像中的深度学习模型,特别是多任务学习场景.
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