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)

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 (MIDL, 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)