Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients. - Verónica Muñoz Ramírez; Nicolas Pinon; Florence Forbes; Carole Lartizien; Michel Dojat (MLCN@MICCAI, 2021)

Brain Subtle Anomaly Detection Based on Auto-Encoders Latent Space Analysis: Application To De Novo Parkinson Patients. - Nicolas Pinon; Geoffroy Oudoumanessah; Robin Trombetta; Michel Dojat; Florence Forbes; Carole Lartizien (ISBI, 2023)

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 (MIDL, 2023)

Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis. - Nicolas Pinon; Robin Trombetta; Carole Lartizien (CoRR, 2023)

Unsupervised anomaly detection in neuroimaging: contributions to representation learning and density support estimation in the latent space. (Détection d'anomalies non supervisée en neuro-imagerie : contributions à l'apprentissage de représentation et à l'estimation de support de densité dans l'espace latent). - Nicolas Pinon (2024)

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; Romain Bouet; Julien Jung; Carole Lartizien (Comput. Methods Programs Biomed., 2025)

OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection. - Nicolas Pinon; Carole Lartizien (CoRR, 2025)