3 papers
cs.CV2026
HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection
Jiayu Huo, Jingyuan Hong, Meng Zhou +2
Anomaly detection is essential for medical image analysis, where pathological regions often appear as rare deviations from normal anatomical structures. While training-based method…
eess.IV2026
DINO-AD: Unsupervised Anomaly Detection with Frozen DINO-V3 Features
Jiayu Huo, Jingyuan Hong, Liyun Chen
Unsupervised anomaly detection (AD) in medical images aims to identify abnormal regions without relying on pixel-level annotations, which is crucial for scalable and label-efficien…
eess.IV2024
SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation
Jiayu Huo, Sebastien Ourselin, Rachel Sparks
Medical image translation is crucial for reducing the need for redundant and expensive multi-modal imaging in clinical field. However, current approaches based on Convolutional Neu…