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

Baptiste Couvy-Duchesne (1, 2, 3) , Johann Faouzi (1) , Benoît Martin (1) , Elina Thibeau-Sutre (1) , Adam Wild (1) , Manon Ansart (1) , Stanley Durrleman​ (1) , Didier Dormont (1, 4) , Ninon Burgos (1) , Olivier Colliot (1)

1 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
2 ICM - Institut du Cerveau = Paris Brain Institute
3 Institute for Molecular Bioscience
4 AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP)

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.

Mots clés: Brain age; MRI; Machine learning; Deep learning; Statistical learning; Ensemble learning

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Machine Learning [stat.ML]

Baptiste Couvy-Duchesne, Johann Faouzi, Benoît Martin, Elina Thibeau-Sutre, Adam Wild, et al.. 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. Frontiers in Psychiatry, 2020, 11, ⟨10.3389/fpsyt.2020.593336⟩. ⟨hal-03136463⟩