1 code implementation • 28 Mar 2024 • Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew, Alexandre Hutton, Roland Wiest, Johannes Kaesmacher, Uta Hanning, Arsany Hakim, Richard Zubal, Waldo Valenzuela, David Robben, Diana M. Sima, Vincenzo Anania, Arne Brys, James A. Meakin, Anne Mickan, Gabriel Broocks, Christian Heitkamp, Shengbo Gao, Kongming Liang, Ziji Zhang, Md Mahfuzur Rahman Siddiquee, Andriy Myronenko, Pooya Ashtari, Sabine Van Huffel, Hyun-su Jeong, Chi-ho Yoon, Chulhong Kim, Jiayu Huo, Sebastien Ourselin, Rachel Sparks, Albert Clèrigues, Arnau Oliver, Xavier Lladó, Liam Chalcroft, Ioannis Pappas, Jeroen Bertels, Ewout Heylen, Juliette Moreau, Nima Hatami, Carole Frindel, Abdul Qayyum, Moona Mazher, Domenec Puig, Shao-Chieh Lin, Chun-Jung Juan, Tianxi Hu, Lyndon Boone, Maged Goubran, Yi-Jui Liu, Susanne Wegener, Florian Kofler, Ivan Ezhov, Suprosanna Shit, Moritz R. Hernandez Petzsche, Bjoern Menze, Jan S. Kirschke, Benedikt Wiestler
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
no code implementations • 22 Feb 2024 • Guillaume Garret, Antoine Vacavant, Carole Frindel
Vascular segmentation represents a crucial clinical task, yet its automation remains challenging.
no code implementations • 16 Mar 2023 • Nima Hatami, Laura Mechtouff, David Rousseau, Tae-Hee Cho, Omer Eker, Yves Berthezene, Carole Frindel
Patient outcome prediction is critical in management of ischemic stroke.
no code implementations • 11 May 2022 • Nima Hatami, Tae-Hee Cho, Laura Mechtouff, Omer Faruk Eker, David Rousseau, Carole Frindel
For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS).
1 code implementation • 20 Jan 2022 • Méghane Decroocq, Carole Frindel, Pierre Rougé, Makoto Ohta, Guillaume Lavoué
We proposed a vessel model based on penalized splines to overcome the limitations inherent to the centerline representation, such as noise and sparsity.
no code implementations • 15 Jun 2021 • Théo Jourdan, Antoine Boutet, Carole Frindel
While this scheme has been proposed as local adaptation to improve the accuracy of the model through local personalization, it has also the advantage to minimize the information about the model exchanged with the server.
1 code implementation • 23 Mar 2020 • Claude Rosin Ngueveu, Antoine Boutet, Carole Frindel, Sébastien Gambs, Théo Jourdan, Claude Rosin
However, nothing prevents the service provider to infer private and sensitive information about a user such as health or demographic attributes. In this paper, we present DySan, a privacy-preserving framework to sanitize motion sensor data against unwanted sensitive inferences (i. e., improving privacy) while limiting the loss of accuracy on the physical activity monitoring (i. e., maintaining data utility).