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Elina Thibeau-Sutre
Researcher at University of Paris
Publications - 14
Citations - 5672
Elina Thibeau-Sutre is an academic researcher from University of Paris. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 5, co-authored 9 publications receiving 3494 citations.
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Journal ArticleDOI
New advances in the Clinica software platform for clinical neuroimaging studies
Alexandre Routier,Arnaud Marcoux,Mauricio Diaz Melo,Jérémy Guillon,Jorge Samper-González,Junhao Wen,Simona Bottani,Alexis Guyot,Elina Thibeau-Sutre,Marc Teichmann,Marie-Odile Habert,Stanley Durrleman,Ninon Burgos,Olivier Colliot +13 more
Journal ArticleDOI
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation.
Junhao Wen,Elina Thibeau-Sutre,Mauricio Diaz-Melo,Jorge Samper-González,Alexandre Routier,Simona Bottani,Didier Dormont,Stanley Durrleman,Ninon Burgos,Olivier Colliot +9 more
TL;DR: The open-source framework for classification of AD using CNN and T1-weighted MRI is extended and found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance.
Journal ArticleDOI
Clinica: an open source software platform for reproducible clinical neuroscience studies
Alexandre Routier,Ninon Burgos,Mauricio Díaz,Michael Bacci,Simona Bottani,Omar El-Rifai,Sabrina Fontanella,Pietro Gori,Jérémy Guillon,Alexis Guyot,Ravi Hassanaly,Thomas Jacquemont,Pascal Lu,Arnaud Marcoux,Tristan Moreau,Jorge Samper-González,Marc Teichmann,Elina Thibeau-Sutre,Ghislain Vaillant,Junhao Wen,Adam Wild,Marie-Odile Habert,Stanley Durrleman,Olivier Colliot +23 more
TL;DR: Clinica is an open-source software platform designed to make clinical neuroscience studies easier and more reproducible, and for researchers to spend less time on data management and processing, and perform reproducible evaluations of their methods.
Journal ArticleDOI
Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review.
Manon Ansart,Stéphane Epelbaum,Giulia Bassignana,Alexandre Bône,Simona Bottani,Tiziana Cattai,Raphaël Couronné,Johann Faouzi,Igor Koval,Maxime Louis,Elina Thibeau-Sutre,Junhao Wen,Adam Wild,Ninon Burgos,Didier Dormont,Olivier Colliot,Stanley Durrleman +16 more
TL;DR: A systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalographic variables significantly improved predictive performance, whereas including other modalities did not show a significant effect.
Posted ContentDOI
Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A Systematic, Quantitative and Critical Review
Manon Ansart,Stéphane Epelbaum,Giulia Bassignana,Alexandre Bône,Simona Bottani,Tiziana Cattai,Raphaël Couronné,Johann Faouzi,Igor Koval,Maxime Louis,Elina Thibeau-Sutre,Junhao Wen,Adam Wild,Ninon Burgos,Didier Dormont,Olivier Colliot,Stanley Durrleman +16 more
TL;DR: A systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalographic variables significantly improved predictive performance, whereas including other modalities did not show a significant effect.