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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

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation.

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

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.

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

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.