Brain age estimation from MRI using a two-stage cascade network with ranking loss
As age increases, human brains will be aged, and people tend to experience cognitive
decline with a higher risk of neuro-degenerative disease and dementia. Recently, it was …
decline with a higher risk of neuro-degenerative disease and dementia. Recently, it was …
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 …
Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI
Estimating brain age and establishing functional biomarkers that are prescient of cognitive
declines resulting from aging and different neurological diseases are still open research …
declines resulting from aging and different neurological diseases are still open research …
A multi‐class deep learning model to estimate brain age while addressing systematic bias of regression to the mean
Background Age‐related changes in human brain may contribute to the development of age‐
related neurodegenerative diseases. It may be possible to estimate “brain age” from …
related neurodegenerative diseases. It may be possible to estimate “brain age” from …
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 …
Age estimation from MR images via 3D convolutional neural network and densely connect
The estimation of brain age from magnetic resonance (MR) images is useful for computer-
aided diagnosis (CAD) in neurodegenerative diseases. Some deep learning methods has …
aided diagnosis (CAD) in neurodegenerative diseases. Some deep learning methods has …
Improving individual brain age prediction using an ensemble deep learning framework
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 …
individual brain health and may serve as a single quantitative index for clinical and domain …
A domain guided CNN architecture for predicting age from structural brain images
Given the wide success of convolutional neural networks (CNNs) applied to natural images,
researchers have begun to apply them to neuroimaging data. To date, however, exploration …
researchers have begun to apply them to neuroimaging data. To date, however, exploration …
Network occlusion sensitivity analysis identifies regional contributions to brain age prediction
L He, C Chen, Y Wang, Q Fan, C Chu, J Xu, L Fan - bioRxiv, 2022 - biorxiv.org
Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently
been used for brain age prediction and have achieved outstanding performance …
been used for brain age prediction and have achieved outstanding performance …
Modeling Life-Span Brain Age from Large-Scale Dataset Based on Multi-level Information Fusion
Predicted brain age could be used to measure individual brain status over development and
degeneration, which could also indicate the potential risk of age-related brain disorders …
degeneration, which could also indicate the potential risk of age-related brain disorders …