no code implementations • 14 Nov 2024 • Soumick Chatterjee, Hendrik Mattern, Marc Dörner, Alessandro Sciarra, Florian Dubost, Hannes Schnurre, Rupali Khatun, Chun-Chih Yu, Tsung-Lin Hsieh, Yi-Shan Tsai, Yi-Zeng Fang, Yung-Ching Yang, Juinn-Dar Huang, Marshall Xu, Siyu Liu, Fernanda L. Ribeiro, Saskia Bollmann, Karthikesh Varma Chintalapati, Chethan Mysuru Radhakrishna, Sri Chandana Hudukula Ram Kumara, Raviteja Sutrave, Abdul Qayyum, Moona Mazher, Imran Razzak, Cristobal Rodero, Steven Niederren, Fengming Lin, Yan Xia, Jiacheng Wang, Riyu Qiu, Liansheng Wang, Arya Yazdan Panah, Rosana El Jurdi, Guanghui Fu, Janan Arslan, Ghislain Vaillant, Romain Valabregue, Didier Dormont, Bruno Stankoff, Olivier Colliot, Luisa Vargas, Isai Daniel Chacón, Ioannis Pitsiorlas, Pablo Arbeláez, Maria A. Zuluaga, Stefanie Schreiber, Oliver Speck, Andreas Nürnberger
The human brain receives nutrients and oxygen through an intricate network of blood vessels.
1 code implementation • 18 Jun 2024 • Sophie Loizillon, Simona Bottani, Stéphane Mabille, Yannick Jacob, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos
Subsequently, three artefact-specific models are pre-trained using these corrupted images to detect distinct types of artefacts.
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.
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.
1 code implementation • 16 Apr 2021 • Simona Bottani, Ninon Burgos, Aurélien Maire, Adam Wild, Sebastian Ströer, Didier Dormont, Olivier Colliot
In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse.
no code implementations • 19 Mar 2020 • Alexandre Morin, Jorge Samper-González, Anne Bertrand, Sebastian Stroer, Didier Dormont, Aline Mendes, Pierrick Coupé, Jamila Ahdidan, Marcel Lévy, Dalila Samri, Harald Hampel, Bruno Dubois, Marc Teichmann, Stéphane Epelbaum, Olivier Colliot
Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader$^{TM}$); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists.
no code implementations • 19 Nov 2019 • Elina Thibeau Sutre, Olivier Colliot, Didier Dormont, Ninon Burgos
We demonstrated that the areas identified by the CNN were consistent with what is known of Alzheimer's disease and that the visualization approach extract coherent longitudinal patterns.
2 code implementations • 16 Apr 2019 • Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo, Jorge Samper-Gonzalez, Alexandre Routier, Simona Bottani, Didier Dormont, Stanley Durrleman, Ninon Burgos, Olivier Colliot
The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.