2 citations · 3 across the 4 of their papers we have counts for
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Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization
Robin Trombetta, Carole Lartizien
Unsupervised anomaly detection aims to detect defective parts of a sample by having access, during training, to a set of normal, i.e. defect-free, data. It has many applications in…
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…
Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains
Robin Trombetta, Olivier Rouvière, Carole Lartizien
Fully supervised deep models have shown promising performance for many medical segmentation tasks. Still, the deployment of these tools in clinics is limited by the very timeconsum…