Explainable Deep Learning for Personalized Age Prediction With Brain Morphology.

ABSTRACT: Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, w

SUBMITTER: Lombardi A 

PROVIDER: S-EPMC8192966 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature