Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge.
Fiche publication
Date publication
octobre 2022
Journal
Diagnostic and interventional imaging
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HOEFFEL Christine
Tous les auteurs :
Mulé S, Lawrance L, Belkouchi Y, Vilgrain V, Lewin M, Trillaud H, Hoeffel C, Laurent V, Ammari S, Morand E, Faucoz O, Tenenhaus A, Cotten A, Meder JF, Talbot H, Luciani A, Lassau N
Lien Pubmed
Résumé
The 2021 edition of the Artificial Intelligence Data Challenge was organized by the French Society of Radiology together with the Centre National d'Études Spatiales and CentraleSupélec with the aim to implement generative adversarial networks (GANs) techniques to provide 1000 magnetic resonance imaging (MRI) cases of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC), a rare and aggressive subtype of HCC, generated from a limited number of real cases from multiple French centers.
Mots clés
Artificial intelligence, Deep learning, Generative adversarial networks, Liver cancer, Magnetic resonance imaging
Référence
Diagn Interv Imaging. 2022 10 4;: