Abstract
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.
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Acknowledgements
The work was supported by the Russian Science Foundation under Grant 19-41-04109.
The considered problem was formulated in the scope of the Project “Machine Learning and Pattern Recognition for the development of diagnostic and clinical prognostic prediction tools in psychiatry, borderline mental disorders, and neurology”, granted by Skoltech Biomedical Initiative Program, Skolkovo Institute of Science and Technology, Moscow, Russia.
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Pominova, M. et al. (2019). Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction. In: Pohl, K., Thompson, W., Adeli, E., Linguraru, M. (eds) Adolescent Brain Cognitive Development Neurocognitive Prediction. ABCD-NP 2019. Lecture Notes in Computer Science(), vol 11791. Springer, Cham. https://doi.org/10.1007/978-3-030-31901-4_19
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