Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework.

ABSTRACT: Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used M

SUBMITTER: Kuo CY 

PROVIDER: S-EPMC8021919 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature