no code implementations • 19 Sep 2024 • Zacharia Mesbah, Léo Mottay, Romain Modzelewski, Pierre Decazes, Sébastien Hapdey, Su Ruan, Sébastien Thureau
For the last three years, the AutoPET competition gathered the medical imaging community around a hot topic: lesion segmentation on Positron Emitting Tomography (PET) scans.
1 code implementation • 24 Apr 2023 • Francesco Cremonesi, Marc Vesin, Sergen Cansiz, Yannick Bouillard, Irene Balelli, Lucia Innocenti, Santiago Silva, Samy-Safwan Ayed, Riccardo Taiello, Laetita Kameni, Richard Vidal, Fanny Orlhac, Christophe Nioche, Nathan Lapel, Bastien Houis, Romain Modzelewski, Olivier Humbert, Melek Önen, Marco Lorenzi
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security.
no code implementations • 11 Apr 2023 • Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski, Fethi Ghazouani, Sébastien Thureau, Maxime Fontanilles, Su Ruan
In this paper, we present a deep learning-based brain tumor recurrence location prediction network.
no code implementations • 1 Mar 2022 • Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan
Conclusions: We show that, by using a multi-task learning approach, we can boost the performance of radiomic analysis by extracting rich information of intratumoral and peritumoral regions.
no code implementations • 19 Mar 2020 • Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski
Using generative adversarial networks (GAN) is a promising way to address this problem, however, it is challenging to train one model to generate different classes of lesions.
no code implementations • 18 Mar 2020 • Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan
In this paper, we present a novel approach to locate different type of lesions in positron emission tomography (PET) images using only a class label at the image-level.