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

• URL: http://arxiv.org/abs/2005.13249v3
• Date: Sun, 16 May 2021 13:12:14 GMT
• Title: CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
• Authors: Dani Kiyasseh, Tingting Zhu, David A. Clifton
• Abstract summary: We propose a family of contrastive learning methods, CLOCS, that encourage representations across space, time, textitand 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. Our training procedure naturally generates patient-specific representations that can be used to quantify patient-similarity.
• Score: 17.58391771585294
• License: http://creativecommons.org/licenses/by/4.0/
• 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, \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.

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