no code implementations • 1 Mar 2024 • Leopold Hebert-Stevens, Gabriel Jimenez, Benoit Delatour, Lev Stimmer, Daniel Racoceanu
This study utilizes graph theory and deep learning to assess variations in Alzheimer's disease (AD) neuropathologies, focusing on classic (cAD) and rapid (rpAD) progression forms.
1 code implementation • 12 Feb 2024 • Yuning Huang, Jingchen Zou, Lanxi Meng, Xin Yue, Qing Zhao, Jianqiang Li, Changwei Song, Gabriel Jimenez, Shaowu Li, Guanghui Fu
Our focus was on understanding the impact of the freezing mechanism on performance.
no code implementations • 25 Feb 2023 • Guanghui Fu, Gabriel Jimenez, Sophie Loizillon, Lydia Chougar, Didier Dormont, Romain Valabregue, Ninon Burgos, Stéphane Lehéricy, Daniel Racoceanu, Olivier Colliot, the ICEBERG Study Group
One may hypothesize that such property can be leveraged for better training of deep learning models.
1 code implementation • 13 Jan 2023 • Gabriel Jimenez, Anuradha Kar, Mehdi Ounissi, Léa Ingrassia, Susana Boluda, Benoît Delatour, Lev Stimmer, Daniel Racoceanu
In this study, we propose a DL-based methodology for semantic segmentation of tau lesions (i. e., neuritic plaques) in WSI of postmortem patients with AD.
no code implementations • 13 Jan 2023 • Gabriel Jimenez, Daniel Racoceanu
Thanks to the recent progress in machine learning algorithms for high-content image processing, computational pathology marks the rise of a new generation of medical discoveries and clinical protocols, including in brain disorders.
no code implementations • 2 Nov 2022 • Guanghui Fu, Gabriel Jimenez, Sophie Loizillon, Rosana El Jurdi, Lydia Chougar, Didier Dormont, Romain Valabregue, Ninon Burgos, Stéphane Lehéricy, Daniel Racoceanu, Olivier Colliot, the ICEBERG Study Group
In this paper, we propose a new model that integrates prior knowledge from different contrasts for red nucleus segmentation.