通过从自由文本报告中学习,在头部CT扫描中自动检测和定位内异常
Aohan Liu1, Yuchen Guo2, Jinhao Lyu3
1School of Software, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, BNRist, Tsinghua University, Beijing 100084, China.
Cell reports. Medicine
|August 31, 2023
概括
这项研究介绍了Cross-DL,这是一个深度学习框架,用于使用文本报告在CT扫描中检测大脑异常. 它在异常检测和定位方面实现了高精度,降低了手动注释成本.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是指放射学
背景情况:
- 医疗图像诊断的深度学习模型需要大量的手动注释,这是昂贵和耗时的.
- 目前的方法在有效利用自由文本临床报告中的丰富信息方面面临挑战.
研究的目的:
- 开发一种新的跨模式学习框架 (Cross-DL),用于在头部CT扫描中检测和定位内异常.
- 利用自由文本图像报告来克服手动图像注释的局限性.
主要方法:
- 交叉DL使用一个分辨器自动从自由文本报告中提取异常标签 (类型和位置).
- 用这些提取的标签使用动态多实例学习方法训练图像分析器.
- 该框架使用了28,472个头部CT扫描的大规模数据集.
主要成果:
- 交叉DL在17个区域中检测了4种异常类型,获得了0.956的接收器运行特征曲线 (AUROC) 下的平均面积.
- 该模型展示了异常的精确的voxel级本地化.
- 对内出血分类的CQ500数据集进行外部验证,结果为AUROC为0.928.
结论:
- 交叉DL有效地利用自由文本报告来训练医学成像中的深度学习模型,大大降低了注释成本.
- 该框架在CT扫描上检测和局部化内异常方面表现出很高的性能.
- 这种方法为医学图像分析提供了一个可扩展的解决方案,可以帮助优先考虑放射学审查.
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