6 papers
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…
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…
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…
ALFRED: Ask a Large-language model For Reliable ECG Diagnosis
Jin Yu, JaeHo Park, TaeJun Park +6
Leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for analyzing medical data, particularly Electrocardiogram (ECG), offers high accuracy and conveni…
New Test-Time Scenario for Biosignal: Concept and Its Approach
Yong-Yeon Jo, Byeong Tak Lee, Beom Joon Kim +3
Online Test-Time Adaptation (OTTA) enhances model robustness by updating pre-trained models with unlabeled data during testing. In healthcare, OTTA is vital for real-time tasks lik…
TADA: Temporal Adversarial Data Augmentation for Time Series Data
Byeong Tak Lee, Joon-myoung Kwon, Yong-Yeon Jo
Domain generalization aim to train models to effectively perform on samples that are unseen and outside of the distribution. Adversarial data augmentation (ADA) is a widely used te…