U-Net Transformer: Self and Cross Attention for Medical Image Segmentation. - Olivier Petit; Nicolas Thome; Clément Rambour; Loic Themyr; Toby Collins; Luc Soler (MLMI@MICCAI, 2021)

Memory Transformers for Full Context and High-Resolution 3D Medical Segmentation. - Loic Themyr; Clément Rambour; Nicolas Thome; Toby Collins; Alexandre Hostettler (MLMI@MICCAI, 2022)

Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation. - Loic Themyr; Clément Rambour; Nicolas Thome; Toby Collins; Alexandre Hostettler (WACV, 2023)

TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research. - William Ndzimbong; Cyril Fourniol; Loic Themyr; Nicolas Thome; Yvonne Keeza; Benoît Sauer; Pierre-Thierry Piechaud; Arnaud Mejean; Jacques Marescaux; Daniel George; Didier Mutter; Alexandre Hostettler; et al. (CoRR, 2023)

Contexte global avec des Transformers pour la segmentation d'images médicales 3D. (Global context with transformers in 3D medical image segmentation). - Loic Themyr (2023)