4 citations · 11 across the 8 of their papers we have counts for
4 papers · 1 filter
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…