CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients

Code

• github.com/danikiyasseh/CLOCS
  OfficialIn paperpytorch★ 58
• github.com/mandiehyewon/goodviews_ecg
  pytorch★ 6

Abstract

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, 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.

Tasks

Reproductions