Cargando…
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...
Autores principales: | , , , , , , , , , |
---|---|
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 |
_version_ | 1783629436391784448 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7770104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT couvyduchesnebaptiste ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT faouzijohann ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT martinbenoit ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT thibeausutreelina ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT wildadam ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT ansartmanon ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT durrlemanstanley ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT dormontdidier ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT burgosninon ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge AT colliotolivier ensemblelearningofconvolutionalneuralnetworksupportvectormachineandbestlinearunbiasedpredictorforbrainagepredictionaramiscontributiontothepredictiveanalyticscompetition2019challenge |