MedImg:公共医疗形象的综合数据库
Bitao Zhong1, Rui Fan1, Yue Ma2
1Department of Biomedical Informatics, Center for Noncoding RNA Medicine, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, Beijing 100191, China.
医疗图像分析的深度学习面临着数据可用性的挑战. 这项研究创建了MedImg,一个包含14种模式的99万张图像的开放数据库,以帮助研究.
科学领域:
- 医学成像
- 人工智能
- 计算机视觉
背景情况:
- 深度学习在医学图像分析方面表现有前途,
- 这些人工智能模型的临床翻译受阻于缺乏可验证的大型,具有良好特征的数据集.
研究的目的:
- 解决医疗成像中的深度学习数据有限的挑战.
- 为医疗图像数据集创建一个集中,可访问的资源.
主要方法:
- 编辑了来自公开来源的105个不同的医学图像数据集.
- 收集了14种模式和13个器官的1,995,671张图像.
- 开发了MedImg在线数据库,以组织并提供对这些精心策划的数据集的开放访问.
主要成果:
- 建立了一个开放的在线平台MedImg (https://www.cuilab.cn/medimg/).
- 该数据库包含来自105个数据集的1,995,671张图像,涵盖了14种医学成像模式.
- 该数据集包括来自13个不同器官的图像, 促进广泛的研究应用.
结论:
- MedImg提供了一个有价值的,系统的资源来克服医学图像分析的深度学习数据的局限性.
- 该平台旨在通过提高数据可访问性来加速人工智能驱动的诊断工具的研究和开发.
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