A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction

TL;DR

This paper introduces a hybrid deep learning and gradient boosting framework to predict fluid intelligence scores from MRI data, achieving competitive accuracy in a community challenge.

Contribution

The novel combination of CNN feature extraction with gradient boosting for fluid intelligence prediction from MRI data.

Findings

Achieved MSE of 18.44 on training set

Achieved MSE of 68.79 on validation set

Achieved MSE of 96.18 on test set

Abstract

The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machine framework to solve this task. We train a convolutional neural network to compress the high dimensional MRI data and learn meaningful image features by predicting the 123 continuous-valued derived data provided with each MRI. These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score. Our approach achieved mean square error (MSE) scores of 18.4374, 68.7868, and 96.1806 for the training, validation, and test set respectively.