Predicting brain age of healthy adults based on structural MRI parcellation using convolutional neural networks
H Jiang, N Lu, K Chen, L Yao, K Li, J Zhang… - Frontiers in …, 2020 - frontiersin.org
Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain
undergoes age-related neuroanatomical changes not only regionally but also on the …
undergoes age-related neuroanatomical changes not only regionally but also on the …
Improved prediction of brain age using multimodal neuroimaging data
Brain age prediction based on imaging data and machine learning (ML) methods has great
potential to provide insights into the development of cognition and mental disorders. Though …
potential to provide insights into the development of cognition and mental disorders. Though …
Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging
Numerous studies have established that estimated brain age constitutes a valuable
biomarker that is predictive of cognitive decline and various neurological diseases. In this …
biomarker that is predictive of cognitive decline and various neurological diseases. In this …
Brain age prediction across the human lifespan using multimodal MRI data
Measuring differences between an individual's age and biological age with biological
information from the brain have the potential to provide biomarkers of clinically relevant …
information from the brain have the potential to provide biomarkers of clinically relevant …
Deep learning and multiplex networks for accurate modeling of brain age
Recent works have extensively investigated the possibility to predict brain aging from T1-
weighted MRI brain scans. The main purposes of these studies are the investigation of …
weighted MRI brain scans. The main purposes of these studies are the investigation of …
Brain age estimation from MRI using cascade networks with ranking loss
Chronological age of healthy people is able to be predicted accurately using deep neural
networks from neuroimaging data, and the predicted brain age could serve as a biomarker …
networks from neuroimaging data, and the predicted brain age could serve as a biomarker …
Anatomical context improves deep learning on the brain age estimation task
Deep learning has shown remarkable improvements in the analysis of medical images
without the need for engineered features. In this work, we hypothesize that deep learning is …
without the need for engineered features. In this work, we hypothesize that deep learning is …
Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan
Brain age estimated by machine learning from T1-weighted magnetic resonance images
(T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early …
(T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early …
Accurate brain age prediction using recurrent slice-based networks
PK Lam, V Santhalingam, P Suresh… - 16th international …, 2020 - spiedigitallibrary.org
BrainAge (a subject's apparent age predicted from neuroimaging data) is an important
biomarker of brain aging. The deviation of BrainAge from true age has been associated with …
biomarker of brain aging. The deviation of BrainAge from true age has been associated with …
[HTML][HTML] Accurate brain‐age models for routine clinical MRI examinations
DA Wood, S Kafiabadi, A Al Busaidi, E Guilhem… - Neuroimage, 2022 - Elsevier
Convolutional neural networks (CNN) can accurately predict chronological age in healthy
individuals from structural MRI brain scans. Potentially, these models could be applied …
individuals from structural MRI brain scans. Potentially, these models could be applied …