In this work, we aimed at predicting children’s fluid intelligence scores based on structural T1-weighted MR images from the largest long-term study of brain development and child health. The target variable was regressed on a data collection site, sociodemographic variables, and brain volume, thus being independent to the potentially informative factors, which were not directly related to the brain functioning. We investigated both feature extraction and deep learning approaches as well as different deep CNN architectures and their ensembles. We proposed an advanced architecture of VoxCNNs ensemble, which yields MSE (92.838) on a blind test.
CITATION STYLE
Pominova, M., Kuzina, A., Kondrateva, E., Sushchinskaya, S., Burnaev, E., Yarkin, V., & Sharaev, M. (2019). Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11791 LNCS, pp. 158–166). Springer. https://doi.org/10.1007/978-3-030-31901-4_19
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