In this work, we aim 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, socio-demographic variables and brain volume, thus being independent to the potentially informative factors, which are not directly related to the brain functioning. We investigate both feature extraction and deep learning approaches as well as different deep CNN architectures and their ensembles. We propose an advanced architecture of VoxCNNs ensemble, which yield MSE (92.838) on blind test.
@article{arxiv.1905.10550,
title = {Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction},
author = {Marina Pominova and Anna Kuzina and Ekaterina Kondrateva and Svetlana Sushchinskaya and Maxim Sharaev and Evgeny Burnaev and and Vyacheslav Yarkin},
journal= {arXiv preprint arXiv:1905.10550},
year = {2019}
}