S More, G Antonopoulos, F Hoffstaedter, J Caspers… - NeuroImage, 2023 - Elsevier
The difference between age predicted using anatomical brain scans and chronological age, ie, the brain-age delta, provides a proxy for atypical aging. Various data representations and …
G Arji, L Erfannia, M Hemmat - Informatics in medicine unlocked, 2023 - Elsevier
Introduction Mental disorders are the main cause of mortality and morbidity worldwide. Deep learning offers a considerable promise for mental health diagnosis and risk assessment. The …
Schizophrenia patients show signs of accelerated aging in cognitive and physiological domains. Both schizophrenia and accelerated aging, as measured by MRI brain images and …
How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) …
Age and gender identification have become a major part of the network, security and care. It has a common use in age specific content access for children. Social media uses it in …
We propose an interpretable 3D Grid-Attention deep neural network that can accurately predict a person's age and whether they have Alzheimer's disease (AD) from a structural …
G Rao, A Li, Y Liu, B Liu - 2020 IEEE 17th International …, 2020 - ieeexplore.ieee.org
Predicting individual chronological age based on neuroimaging data is very promising and important for understanding the trajectory of normal brain development. In this work, we …
Abstract Machine learning models trained on MRI brain scans of healthy subjects can be used to predict age. Accurate estimation of brain age is important for reliably detecting …