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    Biometric contrastive learning for data-efficient deep learning from electrocardiographic images by Sangha, V, Khunte, A, Holste, G, Mortazavi, BJ, Wang, Z, Oikonomou, EK, Khera, R

    Published 2024
    “…</p> <p><strong>Materials and Methods:&nbsp;</strong>Using pairs of ECGs from 78 288 individuals from Yale (2000-2015), we trained a convolutional neural network to identify temporally separated ECG pairs that varied in layouts from the same patient. …”
    Conference item
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