6 citations · 8 across the 6 of their papers we have counts for
3 papers · 1 filter
Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis
Nicolas Pinon, Robin Trombetta, Carole Lartizien
We study several methods for detecting anomalies in color images, constructed on patch-based auto-encoders. Wecompare the performance of three types of methods based, first, on the…
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities
Nicolas Pinon, Robin Trombetta, Carole Lartizien
Anomaly detection remains a challenging task in neuroimaging when little to no supervision is available and when lesions can be very small or with subtle contrast. Patch-based repr…
Brain subtle anomaly detection based on auto-encoders latent space analysis : application to de novo parkinson patients
Nicolas Pinon, Geoffroy Oudoumanessah, Robin Trombetta +3
Neural network-based anomaly detection remains challenging in clinical applications with little or no supervised information and subtle anomalies such as hardly visible brain lesio…