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

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