Predicting Fluid Intelligence from MRI Images with Encoder-Decoder Regularization

In this paper, we develop a 3D convolutional neural network to predict the fluid intelligence from T1-weighted MRI images by adding an encoder-decoder regularization. Considering that cerebellar volume is often highly correlated to intelligence of an individual, we propose to incorporate this morphological information into the framework for fluid intelligence prediction by utilizing an encoder-decoder regularization for brain structure segmentation simultaneously. Specifically, we first train an encoder-decoder network to generate the brain segmentation mask, where the discriminative morphological feature of the brain volume can be learned. Then, we reuse the encoder path of the network as the prediction network backbone for final fluid intelligence prediction by adding an additional regression part to predict the fluid intelligence value. The proposed framework is able to learn the discriminative relationship between the morphological information of brain structures and the intelligence score for more accurate prediction.