6 citations · 8 across the 6 of their papers we have counts for
4 papers · 1 filter
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models
Daria Zotova, Nicolas Pinon, Robin Trombetta +3
Background and Objective. Research in the cross-modal medical image translation domain has been very productive over the past few years in tackling the scarce availability of large…
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…
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients
Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes +2
Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identif…