CLOCS, a contrastive learning method for cardiac signals, consistently outperformed state-of-the-art methods BYOL and SimCLR on downstream tasks and achieved strong generalization with 25% of labelled data.
Cardiac signals
CLOCS (Contrastive Learning of Cardiac Signals) vs BYOL and SimCLR
Performance on downstream tasks (linear evaluation and fine-tuning)
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.
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Dani Kiyasseh
California Institute of Technology
Tingting Zhu
University of Oxford
David A. Clifton
University of Oxford
University of Oxford
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Kiyasseh et al. (Wed,) conducted a other in Cardiac signals. CLOCS (Contrastive Learning of Cardiac Signals) vs. BYOL and SimCLR was evaluated on Performance on downstream tasks (linear evaluation and fine-tuning). CLOCS, a contrastive learning method for cardiac signals, consistently outperformed state-of-the-art methods BYOL and SimCLR on downstream tasks and achieved strong generalization with 25% of labelled data.
synapsesocial.com/papers/6a08000909b3c820153792ba — DOI: https://doi.org/10.48550/arxiv.2005.13249