ArticleIEEE transactions on medical imaging2022
Global-Local Transformer for Brain Age Estimation.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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49 citing papers in PubMed.
- A comparative evaluation of brain MRI modalities, modality-specific measures, brain regions, and cognitive tests for brain age prediction.GeroScience · 2026Article
- OpenMAP-BrainAge: generalizable and interpretable brain age predictor from MRI.Radiology advances · 2026Article
- A multi-modal foundation model for brain disease diagnosis and medical imaging.Patterns (New York, N.Y.) · 2026Article
- superLPNet: a super lightweight parameter deep learning model for brain age estimation from structural MRI.Magma (New York, N.Y.) · 2026Article
- [Research progress on intelligent brain age prediction methods in diagnosis of Parkinson's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Brain age gap as biomarker linking cardiovascular diseases genetic susceptibility and causality.iScience · 2026Article
- From Infancy to Aging: Precise Brain Age Estimation via Hybrid CoTResNet3D and CrossViT Models on T1-Weighted Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- SIG-CFFNet: Structural Information-Guided Cascaded Feature Fusion Network for Gastrointestinal Anatomy Classification.Annals of biomedical engineering · 2026Article
- Neonatal brain age estimation from T2-weighted magnetic resonance images using shallow regression analysis.Scientific reports · 2026Article
- Machine learning to infer neurocognitive testing scores among adolescents and young adults with congenital heart disease.Communications medicine · 2026Article
- MDFNet: a multi-dimensional feature fusion model based on structural magnetic resonance imaging representations for brain age estimation.Magma (New York, N.Y.) · 2026Article
- A token efficient vision framework using patch residual transformer for Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Artificial intelligence approaches in biological age prediction: current status and challenges.British journal of biomedical science · 2026Review
- Mammo-AGE: deep learning estimation of breast age from mammograms.Nature communications · 2025Article
- Brain age gap as a predictive biomarker that links aging, lifestyle, and neuropsychiatric health.Communications medicine · 2025Article
- Do Transformers and CNNs Learn Different Concepts of Brain Age?Human brain mapping · 2025Article
- Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction.Human brain mapping · 2025Article
- Vision transformer and deep learning based weighted ensemble model for automated spine fracture type identification with GAN generated CT images.Scientific reports · 2025Article
- MRI-based habitat radiomics combined with vision transformer for identifying vulnerable intracranial atherosclerotic plaques and predicting stroke events: a multicenter, retrospective study.EClinicalMedicine · 2025Article
- Genetically supported targets and drug repurposing for brain aging: A systematic study in the UK Biobank.Science advances · 2025Article
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3 authors.
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Abstract
Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract the global-context information from the whole input image and a local-pathway to extract the local fine-grained details from local patches. The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age. We evaluate the proposed method on 8 public datasets with 8,379 healthy brain MRIs with the age range of 0-97 years. 6 datasets are used for cross-validation and 2 datasets are used for evaluating the generality. Comparing with other state-of-the-art methods, the proposed global-local transformer reduces the mean absolute error of the estimated ages to 2.70 years and increases the correlation coefficient of the estimated age and the chronological age to 0.9853. In addition, our proposed method provides regional information of which local patches are most informative for brain age estimation. Our source code is available on: https://github.com/shengfly/global-local-transformer.
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