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Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the nu...

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Autores principales: Couvy-Duchesne, Baptiste, Faouzi, Johann, Martin, Benoît, Thibeau–Sutre, Elina, Wild, Adam, Ansart, Manon, Durrleman, Stanley, Dormont, Didier, Burgos, Ninon, Colliot, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7770104/
https://www.ncbi.nlm.nih.gov/pubmed/33384629
http://dx.doi.org/10.3389/fpsyt.2020.593336
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author Couvy-Duchesne, Baptiste
Faouzi, Johann
Martin, Benoît
Thibeau–Sutre, Elina
Wild, Adam
Ansart, Manon
Durrleman, Stanley
Dormont, Didier
Burgos, Ninon
Colliot, Olivier
author_facet Couvy-Duchesne, Baptiste
Faouzi, Johann
Martin, Benoît
Thibeau–Sutre, Elina
Wild, Adam
Ansart, Manon
Durrleman, Stanley
Dormont, Didier
Burgos, Ninon
Colliot, Olivier
author_sort Couvy-Duchesne, Baptiste
collection PubMed
description We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.
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spelling pubmed-77701042020-12-30 Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge Couvy-Duchesne, Baptiste Faouzi, Johann Martin, Benoît Thibeau–Sutre, Elina Wild, Adam Ansart, Manon Durrleman, Stanley Dormont, Didier Burgos, Ninon Colliot, Olivier Front Psychiatry Psychiatry We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status. Frontiers Media S.A. 2020-12-15 /pmc/articles/PMC7770104/ /pubmed/33384629 http://dx.doi.org/10.3389/fpsyt.2020.593336 Text en Copyright © 2020 Couvy-Duchesne, Faouzi, Martin, Thibeau–Sutre, Wild, Ansart, Durrleman, Dormont, Burgos and Colliot. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Couvy-Duchesne, Baptiste
Faouzi, Johann
Martin, Benoît
Thibeau–Sutre, Elina
Wild, Adam
Ansart, Manon
Durrleman, Stanley
Dormont, Didier
Burgos, Ninon
Colliot, Olivier
Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge
title Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge
title_full Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge
title_fullStr Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge
title_full_unstemmed Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge
title_short Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge
title_sort ensemble learning of convolutional neural network, support vector machine, and best linear unbiased predictor for brain age prediction: aramis contribution to the predictive analytics competition 2019 challenge
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7770104/
https://www.ncbi.nlm.nih.gov/pubmed/33384629
http://dx.doi.org/10.3389/fpsyt.2020.593336
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