Existing models for classifying and interpreting cognitive intelligence based on pediatric brain images are usually derived from low-dimensional statistical analysis. While such models are computationally efficient, they use oversimplified representations of a brain's features. They neglect essential brain structure information, such as regions of interest (ROI) and high-density segmentation features. Therefore, we develop a deep learning framework to understand and model cognitive intelligence using CT brain images.
Our data pipeline provides over 600 billion parameters. Such high-density data requires a novel parallel computing framework for tuning and training tasks. Our framework can tractably handle these computational requirements by utilizing 1) an extensive grid search fitting-training scheme, 2) automated learning that optimizes deep neural network structure, 3) Bayesian variation inference that interprets uncertainty during the learning process, and 4) hardware configurations for both CPU and GPU environments. This framework is adaptable and particularly useful for high- and low-dimensional datasets shown in many cognitive modellings.
We will demonstrate the predictive and descriptive capability of such a deep learning framework. One of this research highlights is that we have successfully modelled the uncertainty of the latent intelligence features using ELBO optimization, transformed an integration of a joint distribution into an expectation function.