公共数据同质化用于AI模型开发乳腺癌
Vassilis Kilintzis1, Varvara Kalokyri2, Haridimos Kondylakis2
1Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece. billyk@ics.forth.gr.
European radiology experimental
|April 8, 2024
概括
这项研究创建了最大的同质化公共数据集,用于乳腺癌AI开发. 该RV-Cherry-Picker平台提供统一访问临床和成像数据,简化AI模型创建.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 瘤成像学成像学
背景情况:
- 开发可靠的AI用于临床使用需要强大的数据. 公开可用的数据集,如来自癌症成像档案 (TCIA) 的数据集,提供了潜在的可能性,但由于异质性,需要协调.
- 乳腺癌研究受益于可访问的成像和临床数据,但现有的档案很难过以满足特定的人工智能模型开发需求.
研究的目的:
- 开发乳腺癌的一致化数据集,整合临床和成像数据用于AI模型开发.
- 创建一个可搜索的平台,用于访问和过乳腺癌成像和临床数据.
主要方法:
- 从TCIA获得并协调了五个数据集.
- 开发了一种共同的数据模型和提取-转换-负载过程,用于临床数据同质化.
- 从磁共振成像 (MRI) 数据中提取并提供可搜索的数字成像和医学通信 (DICOM) 信息.
主要成果:
- 创建了一个均质化的数据集,包括来自2,035名乳腺癌患者的数据.
- 开发了RV-Cherry-Picker平台,用于统一搜索和下载临床和成像数据集.
- 启用特定成像系列的选择 (例如,动态对比增强) 用于AI模型开发.
结论:
- RV-Cherry-Picker为乳腺癌人工智能开发提供了最大的,公开可用的,同质化的成像和临床数据集.
- 提出的方法方便创建用于AI模型开发的合并公共数据集,以乳腺癌MRI数据为例.
- 该平台提供了对乳腺MRI数据的统一访问和详细选择,大大帮助AI模型开发.
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