44 citations · 107 across the 17 of their papers we have counts for
12 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…
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…
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization
Chaoqin Huang, Qinwei Xu, Yanfeng Wang +2
Recently, anomaly detection and localization in multimedia data have received significant attention among the machine learning community. In real-world applications such as medical…
Self-supervised Tumor Segmentation through Layer Decomposition
Xiaoman Zhang, Weidi Xie, Chaoqin Huang +4
In this paper, we target self-supervised representation learning for zero-shot tumor segmentation. We make the following contributions: First, we advocate a zero-shot setting, wher…
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
Fei Ye, Huangjie Zheng, Chaoqin Huang +1
Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consisten…
Attribute Restoration Framework for Anomaly Detection
Chaoqin Huang, Fei Ye, Jinkun Cao +3
With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods h…
Recurrent Residual Module for Fast Inference in Videos
Bowen Pan, Wuwei Lin, Xiaolin Fang +3
Deep convolutional neural networks (CNNs) have made impressive progress in many video recognition tasks such as video pose estimation and video object detection. However, CNN infer…