Search Results for author:
Found 7 papers, 3 papers with code
Hierarchical Gaussian Mixture Normalizing Flow Modeling for Unified Anomaly Detection
In this paper, we propose a novel Hierarchical Gaussian mixture normalizing flow modeling method for accomplishing unified Anomaly Detection, which we call HGAD.
Focus the Discrepancy: Intra- and Inter-Correlation Learning for Image Anomaly Detection
Humans recognize anomalies through two aspects: larger patch-wise representation discrepancies and weaker patch-to-normal-patch correlations.
A portable widefield fundus camera with high dynamic range imaging capability
Limited image contrast and field of view (FOV) are common limitations of conventional fundus cameras, making it difficult to detect subtle abnormalities at the early stages of eye diseases.
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection
In this way, our model can form a more explicit and discriminative decision boundary to distinguish known and also unseen anomalies from normal samples more effectively.
Ranked #3 on
Supervised Anomaly Detection
on MVTec AD
(using extra training data)
Quantitative optical coherence tomography reveals rod photoreceptor degeneration in early diabetic retinopathy
Methods: OCT images were acquired from normal eyes, diabetic eyes with no diabetic retinopathy (NoDR) and mild DR. Quantitative features, including length features quantifying band distances and reflectance intensity features among the external limiting membrane (ELM), inner segment ellipsoid (ISe), interdigitation zone (IZ) and retinal pigment epithelium (RPE) were determined.
Depth-resolved vascular profile features for artery-vein classification in OCT and OCT angiography of human retina
Uniform lumen intensity was observed in both small and large arteries.
