11 citations
- Centre de Recherche en Acquisition et Traitement de l'Image pour la SantéFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Hospices Civils de LyonFR1 paper
- InsermFR1 paper
- Institut National de la StatistiqueMG1 paper
- Institut National des Sciences Appliquées de LyonFR1 paper
- Laboratoire d’Imagerie BiomédicaleFR1 paper
- Laboratoire d'Informatique en Images et Systèmes d'InformationFR1 paper
- Laboratory for Atmospheric and Space PhysicsUS1 paper
- Lyon 1 UniversitéFR1 paper
- Myriad (Germany)DE1 paper
- Signify (Netherlands)NL1 paper
4 papers · 1 filter
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…
Whole-brain radiomics for clustered federated personalization in brain tumor segmentation
Matthis Manthe, Stefan Duffner, Carole Lartizien
Federated learning and its application to medical image segmentation have recently become a popular research topic. This training paradigm suffers from statistical heterogeneity be…
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…