# Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction

## TL;DR

This paper develops an ensemble of 3D CNN regressors with data fusion to predict children's fluid intelligence scores from MRI images, achieving a low mean squared error on unseen data.

## Contribution

It introduces an advanced VoxCNN ensemble architecture that effectively combines features and deep learning for brain-based intelligence prediction.

## Key findings

- Achieved an MSE of 92.838 on blind test data.
- Demonstrated the effectiveness of ensemble deep learning models.
- Validated approach on a large, long-term brain development dataset.

## Abstract

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.