3 papers
cs.LG2026
ExECG: An Explainable AI Framework for ECG models
Jong-Hwan Jang, Yong-yeon Jo
Deep learning has enabled ECG diagnostic models with strong performance in tasks such as arrhythmia classification and abnormality detection. However, accuracy alone is insufficien…
cs.AI2025
CoFE: A Framework Generating Counterfactual ECG for Explainable Cardiac AI-Diagnostics
Jong-Hwan Jang, Junho Song, Yong-Yeon Jo
Recognizing the need for explainable AI (XAI) approaches to enable the successful integration of AI-based ECG prediction models (AI-ECG) into clinical practice, we introduce a fram…
cs.LG2025
CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains
Junho Song, Jong-Hwan Jang, DongGyun Hong +2
Electrocardiogram (ECG) diagnosis remains challenging due to limited labeled data and the need to capture subtle yet clinically meaningful variations in rhythm and morphology. We p…