SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification
Researchers have developed SafeECGMatch, a novel semi-supervised learning framework designed for electrocardiogram (ECG) classification. This method addresses the challenge of limited labeled data in clinical settings by effectively handling unlabeled data that may contain out-of-distribution anomalies. SafeECGMatch utilizes a dual-branch architecture to extract time-frequency representations and incorporates adaptive calibration techniques to ensure reliable OOD rejection and accurate pseudo-labeling. AI
IMPACT Enhances the reliability of AI models in medical diagnostics by improving their ability to handle unseen data.