4 papers
Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models
Tai Le-Gia
Zero-shot anomaly detection (ZSAD) is attractive for medical imaging because clinical systems must handle heterogeneous acquisition protocols, changing patient populations, and pat…
Training-Free Zero-Shot Anomaly Detection in 3D Brain MRI with 2D Foundation Models
Tai Le-Gia, Jaehyun Ahn
Zero-shot anomaly detection (ZSAD) has gained increasing attention in medical imaging as a way to identify abnormalities without task-specific supervision, but most advances remain…
On the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection
Tai Le-Gia
Zero-shot anomaly classification and segmentation (AC/AS) aim to detect anomalous samples and regions without any training data, a capability increasingly crucial in industrial ins…
On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection
Tai Le-Gia, Ahn Jaehyun
Zero-shot image anomaly classification (AC) and segmentation (AS) are vital for industrial quality control, detecting defects without prior training data. Existing representation-b…