通过主动学习和伪标签来检测肺部喷液的深度学习模型:一个多站点研究
Joseph Chang1,2, Bo-Ru Lin3, Ti-Hao Wang4,5,6
1Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei 100, 100, Taipei, Taiwan.
BMC medical imaging
|April 19, 2024
概括
一个新的深度学习算法用于检测胸部X射线中的胸腔溢液,其准确性很高. 这种计算机辅助分类 (CADt) 系统使用主动学习来减少专家的工作量,提高诊断效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 在美国每年有150万人患有多流,需要及时诊断.
- 当前的诊断方法需要及时,准确地检测到流.
- 开发临床级算法来检测肺溢液的发展至关重要.
研究的目的:
- 开发和验证一种基于深度学习的计算机辅助选 (CADt) 算法,用于检测肺部溢液.
- 利用积极学习 (AL) 框架来提高CADt算法开发的效率.
- 解决对临床级算法的需求,以便及时诊断腹腔溢液.
主要方法:
- 一个深度学习算法被训练在10599张来自台湾的胸部X射线图 (2006-2018) 上.
- 积极学习 (AL) 框架被采用,以尽量减少对专家注释的要求.
- 外部验证是在美国和台湾22个临床场所的600张胸部X射线图上进行的.
主要成果:
- CADt算法实现了高诊断性能:灵敏度为0.95,特异性为0.97.
- 接收器操作特征曲线 (AUC) 下的面积为0.97,表明了非常好的准确性.
- 该算法在不同的人口统计和临床环境中表现出强的性能.
结论:
- 一个新的,基于主动学习的CADt算法用于部溢血诊断成功开发和验证.
- 基于AL的CADt系统实现了高精度,同时大大减少了临床专家的注释工作量.
- 这种方法提高了利用先进技术进行快速和准确的医学诊断的可行性.
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