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20232026
most citedAnomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning

2 citations · 2 across the 5 of their papers we have counts for

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5 papers

eess.IV2026

A Unified DINOv2-Based Framework for LVEF Estimation, GLS Dysfunction Classification, and Early Cardiotoxicity Prediction

Xiaotong Zhang, Mingyue Cui, Qing Cao +1

Left ventricular ejection fraction (LVEF) estimation (Task 1), global longitu-dinal strain (GLS)-based dysfunction classification (Task 2), and early cardi-otoxicity prediction (Ta…

cs.CV2024

EchoNarrator: Generating natural text explanations for ejection fraction predictions

Sarina Thomas, Qing Cao, Anna Novikova +2

Ejection fraction (EF) of the left ventricle (LV) is considered as one of the most important measurements for diagnosing acute heart failure and can be estimated during cardiac ult…

cs.CV2024

Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG Diagnosis

Aofan Jiang, Chaoqin Huang, Qing Cao +5

Current computer-aided ECG diagnostic systems struggle with the underdetection of rare but critical cardiac anomalies due to the imbalanced nature of ECG datasets. This study intro…

cs.CV2024★ 2 cited

Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning

Aofan Jiang, Chaoqin Huang, Qing Cao +5

The electrocardiogram (ECG) is an essential tool for diagnosing heart disease, with computer-aided systems improving diagnostic accuracy and reducing healthcare costs. Despite adva…

cs.CV2023

Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection

Aofan Jiang, Chaoqin Huang, Qing Cao +5

Electrocardiogram (ECG) is a widely used diagnostic tool for detecting heart conditions. Rare cardiac diseases may be underdiagnosed using traditional ECG analysis, considering tha…