mlsanityData to 7 Oct 2026

Global-Local Transformer for Brain Age Estimation

arXiv 2109.01663 ↗First posted 3 Sep 2021cs.CV eess.IVcited by 34

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Abstract (arXiv)

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: urlhttps://github.com/shengfly/global-local-transformer.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 26 of its 69 references that are in the map. First posted on arXiv 3 Sep 2021.

Builds on

26 of 69 references.

References · 26

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14 more
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Citing papers per year: 2022: 9; 2023: 6; 2024: 6; 2025: 8; 2026: 5.

34 papers.

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  1. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  2. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  3. 2022Transformer Meets Boundary Value Inverse Problemscited by 22
  4. 2022Deep Relation Learning for Regression and Its Application to Brain Age Estimationcited by 12
  5. 2022RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detectioncited by 12
  6. 2022Deep Learning for Brain Age Estimation: A Systematic Reviewcited by 12
  7. 2023Is attention all you need in medical image analysis? A reviewcited by 12
  8. 2022Vision Transformers in Medical Imaging: A Reviewcited by 8
  9. 2023U-Netmer: U-Net meets Transformer for medical image segmentationcited by 6
  10. 2024Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domaincited by 6
  11. 2025ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specializationcited by 6
  12. 2024Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIscited by 5
22 more
  1. 2023TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Predictioncited by 3
  2. 2023SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixupcited by 3
  3. 2023A voxel-level approach to brain age prediction: A method to assess regional brain agingcited by 3
  4. 2024Lesion-Aware Cross-Phase Attention Network for Renal Tumor Subtype Classification on Multi-Phase CT Scanscited by 3
  5. 2024Dual Graph Attention based Disentanglement Multiple Instance Learning for Brain Age Estimationcited by 2
  6. 2024Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasetscited by 2
  7. 2022OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXtcited by 1
  8. 2025Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learningcited by 1
  9. 2026VariViT: A Vision Transformer for Variable Image Sizescited by 1
  10. 2026Following the Diagnostic Trace: Visual Cognition-guided Cooperative Network for Chest X-Ray Diagnosiscited by 1
  11. 2022Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volumecited by 0
  12. 2023Multi-task Collaborative Pre-training and Individual-adaptive-tokens Fine-tuning: A Unified Framework for Brain Representation Learningcited by 0
  13. 2024Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimagingcited by 0
  14. 2025OpenMAP-BrainAge: Generalizable and Interpretable Brain Age Predictorcited by 0
  15. 2025Robust brain age estimation from structural MRI with contrastive learningcited by 0
  16. 2025Deformable Convolution Module with Globally Learned Relative Offsets for Fundus Vessel Segmentationcited by 0
  17. 2025Deeply Supervised Multi-Task Autoencoder for Biological Brain Age estimation using three dimensional T1-weighted magnetic resonance imagingcited by 0
  18. 2025BrainRotViT: Transformer-ResNet Hybrid for Explainable Modeling of Brain Aging from 3D sMRIcited by 0
  19. 2025MRI-Based Brain Age Estimation with Supervised Contrastive Learning of Continuous Representationcited by 0
  20. 2026SurfAge-Net: A Hierarchical Surface-Based Network for Interpretable Fine-Grained Brain Age Predictioncited by 0
  21. 2026Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRIcited by 0
  22. 2026Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentationcited by 0