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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.
Author Information
Dani Kiyasseh (University of Oxford)
Tingting Zhu (University of Oxford)
David Clifton (University of Oxford)
Related Events (a corresponding poster, oral, or spotlight)
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2021 Poster: CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients »
Thu. Jul 22nd 04:00 -- 06:00 PM Room
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