论文标题
Clocs：对跨空间，时间和患者心脏信号的对比度学习
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
论文作者
论文摘要
医疗保健行业产生了无标记的生理数据的曲折。可以通过对比度学习来利用这些数据，这是一种自我监督的预训练方法，鼓励表示实例相似。我们提出了一种对比的学习方法，即Clocs，它鼓励跨时空，时间，\ textit {and}患者彼此相似。我们表明，在执行对下游任务的线性评估和微调时，CLOC始终优于最先进的方法BYOL和SIMCLR。我们还表明，CLOC仅使用标记的培训数据的25％实现强大的概括性能。此外，我们的培训程序自然会产生特定于患者的表示，可用于量化患者相似性。
The healthcare industry generates troves of unlabelled physiological data. This data can be exploited via contrastive learning, a self-supervised pre-training method that encourages representations of instances to be similar to one another. We propose a family of contrastive learning methods, CLOCS, that encourages representations across space, time, \textit{and} patients to be similar to one another. We show that CLOCS consistently outperforms the state-of-the-art methods, BYOL and SimCLR, when performing a linear evaluation of, and fine-tuning on, downstream tasks. We also show that CLOCS achieves strong generalization performance with only 25\% of labelled training data. Furthermore, our training procedure naturally generates patient-specific representations that can be used to quantify patient-similarity.
