MSAIC-Net combines parallel atrous convolutions, channel attention, and supervised contrastive learning to outperform baselines on ECG-based myocardial substrate abnormality detection, with larger gains in a low-data institutional cohort.
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Fine-tuning a global DL model on patient-specific ECG segments raises AUROC for 5-minute AF prediction from 0.614 to 0.711 (ICENTIA11K) and 0.585 to 0.686 (MobiCARE).
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MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection
MSAIC-Net combines parallel atrous convolutions, channel attention, and supervised contrastive learning to outperform baselines on ECG-based myocardial substrate abnormality detection, with larger gains in a low-data institutional cohort.
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Personalized Deep Learning for Short-Term Forecasting of Impending Atrial Fibrillation from Continuous Wearable ECG Signals
Fine-tuning a global DL model on patient-specific ECG segments raises AUROC for 5-minute AF prediction from 0.614 to 0.711 (ICENTIA11K) and 0.585 to 0.686 (MobiCARE).