2 citations · 2 across the 3 of their papers we have counts for
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cs.CV2026
Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis
Chaoqin Huang, Zi Zeng, Aofan Jiang +6
Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts and demographic disparities i…
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…