11 citations
- Laboratoire d’Imagerie BiomédicaleFR2 papers
- Myriad (Germany)DE2 papers
- Centre de Recherche en Acquisition et Traitement de l'Image pour la SantéFR1 paper
- Centre de Recherche en Neurosciences de LyonFR1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- City College of New YorkUS1 paper
- City University of New YorkUS1 paper
- HCL Technologies (India)IN1 paper
- Hospices Civils de LyonFR1 paper
- InsermFR1 paper
- Institut de MyologieFR1 paper
- Institut National de la StatistiqueMG1 paper
9 papers
OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection
Nicolas Pinon, Robin Trombetta, Carole Lartizien
Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not availa…
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…
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…
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…