4 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
Optimizing Neural Network Scale for ECG Classification
Byeong Tak Lee, Yong-Yeon Jo, Joon-Myoung Kwon
We study scaling convolutional neural networks (CNNs), specifically targeting Residual neural networks (ResNet), for analyzing electrocardiograms (ECGs). Although ECG signals are t…