Biometric contrastive learning for data-efficient deep learning from electrocardiographic images.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 38269618

Abstract

OBJECTIVE: Artificial intelligence (AI) detects heart disease from images of electrocardiograms (ECGs). However, traditional supervised learning is limited by the need for large amounts of labeled data. We report the development of Biometric Contrastive Learning (BCL), a self-supervised pretraining approach for label-efficient deep learning on ECG images.

Authors

  • Veer Sangha
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
  • Akshay Khunte
    Department of Computer Science, Yale University, New Haven, CT, 06511, United States.
  • Gregory Holste
    Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
  • Bobak J Mortazavi
    Texas A&M University, USA.
  • Zhangyang Wang
    Departments of Electrical and Computer Engineering & Computer Science and Engineering Texas A&M University, College Station, TX 77840.
  • Evangelos K Oikonomou
    Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, UK.
  • Rohan Khera
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

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