以共识为指导的自我监督学习在心声回声学细分中的评估.
Preshen Naidoo1, Patricia Fernandes1, Nasim Dadashi Serej1
1THRIVE Centre, University of West London, London, United Kingdom.
Computers in biology and medicine
|October 2, 2025
概括
自主监督学习,特别是对比式学习,在回声心电图中使用最小的标记数据来增强左心室细分. 人工智能模型通过对专家注释进行概括来实现可靠的评估.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 医疗图像细分 医疗图像细分
背景情况:
- 左心室细分对于回声心脏学中的心脏功能评估至关重要.
- 深度学习需要大量的注释数据集,这些数据集的创建是昂贵和耗时的.
- 自主监督学习 (SSL) 提供了一种利用未标记数据的方法,但其在左心室细分中的应用需要进一步探索.
研究的目的:
- 调查各种SSL借口任务对心声回声细分的有效性.
- 分析未标记数据集大小和分布对模型预培训的影响.
- 引入一个新的多专家注释数据集,用于强大的细分评估.
主要方法:
- 不同的SSL方法用于心声回声细分的比较.
- 用不同数量的未标记数据评估培训前的表现.
- 开发基于共识的注释策略,使用多名专家来减少噪音.
主要成果:
- 对比学习在SSL方法中表现出优越的性能,特别是在低标签场景中.
- 使用SSL进行预训练并对15%的标记数据进行微调的AI模型比单个专家更好地同意多专家的共识.
- 在预训练期间过多的未标记的数据可能会因为冗余而对表现产生负面影响.
结论:
- 用SSL训练的AI模型可以在各种专家注释中有效地概括.
- 这种方法导致更可靠和可重复的心脏功能评估.
- SSL是一种可行的策略,可以减少对大规模手动注释数据集的依赖,用于心声回声细分.
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