Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge.

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

https://www.ncbi.nlm.nih.gov/pubmed/36207277

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;: