Deep patch-based priors under a fully convolutional encoder-decoder architecture for interstitial lung disease segmentation. - Maria Vakalopoulou; Guillaume Chassagnon; Nikos Paragios; Marie-Pierre Revel; Evangelia I. Zacharaki (ISBI, 2018)

Linear and Deformable Image Registration with 3D Convolutional Neural Networks. - Stergios Christodoulidis; Mihir Sahasrabudhe; Maria Vakalopoulou; Guillaume Chassagnon; Marie-Pierre Revel; Stavroula G. Mougiakakou; Nikos Paragios (RAMBO+BIA+TIA@MICCAI, 2018)

AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation. - Maria Vakalopoulou; Guillaume Chassagnon; Norbert Bus; Rafael Marini; Evangelia I. Zacharaki; Marie-Pierre Revel; Nikos Paragios (MICCAI (4), 2018)

AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia. - Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice André; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; et al. (CoRR, 2020)

Exploring Deep Registration Latent Spaces. - Théo Estienne; Maria Vakalopoulou; Stergios Christodoulidis; Enzo Battistella; Théophraste Henry; Marvin Lerousseau; Amaury Leroy; Guillaume Chassagnon; Marie-Pierre Revel; Nikos Paragios; Eric Deutsch (DART/FAIR@MICCAI, 2021)

Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment. - Othmane Laousy; Guillaume Chassagnon; Edouard Oyallon; Nikos Paragios; Marie-Pierre Revel; Maria Vakalopoulou (MLMI@MICCAI, 2021)

Certification of Deep Learning Models for Medical Image Segmentation. - Othmane Laousy; Alexandre Araujo; Guillaume Chassagnon; Nikos Paragios; Marie-Pierre Revel; Maria Vakalopoulou (MICCAI (4), 2023)

Towards better certified segmentation via diffusion models. - Othmane Laousy; Alexandre Araujo; Guillaume Chassagnon; Marie-Pierre Revel; Siddharth Garg; Farshad Khorrami; Maria Vakalopoulou (UAI, 2023)

Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning. - Théo Moutakanni; Piotr Bojanowski; Guillaume Chassagnon; Céline Hudelot; Armand Joulin; Yann LeCun; Matthew J. Muckley; Maxime Oquab; Marie-Pierre Revel; Maria Vakalopoulou (CoRR, 2024)

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data. - Luuk H. Boulogne; Julian Lorenz; Daniel Kienzle; Robin Schön; Katja Ludwig; Rainer Lienhart; Simon Jégou; Guang Li; Cong Chen; Qi Wang; Derik Shi; Mayug Maniparambil; et al. (Medical Image Anal., 2024)