4 citations · 11 across the 8 of their papers we have counts for
8 papers
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…
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…
Few-Shot Anomaly Detection via Category-Agnostic Registration Learning
Chaoqin Huang, Haoyan Guan, Aofan Jiang +4
Most existing anomaly detection (AD) methods require a dedicated model for each category. Such a paradigm, despite its promising results, is computationally expensive and inefficie…
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…
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images
Chaoqin Huang, Aofan Jiang, Jinghao Feng +3
Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains. However, th…
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…
Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection
Chaoqin Huang, Aofan Jiang, Ya Zhang +1
Anomaly detection has gained considerable attention due to its broad range of applications, particularly in industrial defect detection. To address the challenges of data collectio…
Registration based Few-Shot Anomaly Detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang +3
This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided…