Search papers, labs, and topics across Lattice.
Electrophysiology Section, Division of Cardiology, University of Washington , Seattle, WA ,
2
2
3
1
This study establishes SSL as a promising paradigm for ECG analysis, particularly in settings with limited annotated data, enhancing accessibility, generalizability, and fairness in AI-driven cardiac diagnostics across diverse clinical environments and questions.
Self-supervised learning beats supervised learning for ECG interpretation when labeled data is scarce, unlocking more robust and generalizable AI-driven cardiac diagnostics.
