CPSS:融合一致性规范化和伪标记技术,用于半监督的深度心血管疾病检测,使用所有未标记的心电图
Jiguang Shi1, Wenhan Liu1, Huaicheng Zhang1
1School of Physics and Technology, Wuhan University, Wuhan, 430072, China.
Computer methods and programs in biomedicine
|July 11, 2024
概括
这项研究引入了一种新型的半监督学习 (SSL) 算法,CPSS,以改善通过心电图 (ECG) 检测心血管疾病 (CVD). CPSS有效地利用未标记的ECG数据,大大减少了手动标记的需要,并使实时的心血管疾病监测成为可能.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
背景情况:
- 对心电图 (ECG) 分析的监督深度学习需要广泛的标记数据.
- 手动心电图标记是耗时的,需要专门的医疗专业知识.
- 半监督学习 (SSL) 通过利用未标记的数据提供了一个解决方案.
研究的目的:
- 通过最大限度地利用未标记的心电图数据来提高心血管疾病 (CVD) 检测性能.
- 开发一种有效的方法,减少对大型手动标记的心电图数据集的依赖.
主要方法:
- 开发了一种新的SSL算法CPSS,它结合了一致性规范化和伪标签技术.
- 该方法包括在标记的ECG上进行监督学习,以及在增强的未标记的ECG上进行无监督学习.
- 在VGGNet和ResNet分类器中,使用标记和未标记数据进行了联合优化.
主要成果:
- 与监督学习相比,CPSS显著提高了准确性,在多个数据库中仅有10%的ECG被标记.
- 实现了与完全监督的方法可比的性能,同时将标签工作量减少了90%.
- 在SoC上使用训练有素的分类器开发了一个实时CVD监控系统.
结论:
- 拟议的CPSS算法有效地提高了使用未标记的ECG数据进行心血管疾病检测.
- 在深度学习应用中,CPSS显著减轻了ECG标签的负担.
- 开发的监测系统强调了CPSS的实际,现实世界的适用性.
相关概念视频
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