# Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIs

arXiv: https://arxiv.org/abs/2401.09475 · HTML: https://mlsanity.com/paper/2401.09475/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision](https://mlsanity.com/paper/2109.08203/) (2021)
- [Global-Local Transformer for Brain Age Estimation](https://mlsanity.com/paper/2109.01663/) (2021)
- [Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss](https://mlsanity.com/paper/2106.03052/) (2021)
- [Deep Learning for Brain Age Estimation: A Systematic Review](https://mlsanity.com/paper/2212.03868/) (2022)
- [Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives](https://mlsanity.com/paper/2108.07711/) (2021)

## Built on by (most cited first, 5 of 5)

- [ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization](https://mlsanity.com/paper/2511.10971/) (2025)
- [Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasets](https://mlsanity.com/paper/2407.04808/) (2024)
- [Deeply Supervised Multi-Task Autoencoder for Biological Brain Age estimation using three dimensional T1-weighted magnetic resonance imaging](https://mlsanity.com/paper/2508.01565/) (2025)
- [BrainRotViT: Transformer-ResNet Hybrid for Explainable Modeling of Brain Aging from 3D sMRI](https://mlsanity.com/paper/2511.15188/) (2025)
- [Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI](https://mlsanity.com/paper/2606.26894/) (2026)

## Abstract (arXiv)

The integration of machine learning in medicine has significantly improved diagnostic precision, particularly in the interpretation of complex structures like the human brain. Diagnosing challenging conditions such as Alzheimer's disease has prompted the development of brain age estimation techniques. These methods often leverage three-dimensional Magnetic Resonance Imaging (MRI) scans, with recent studies emphasizing the efficacy of 3D convolutional neural networks (CNNs) like 3D ResNet. However, the untapped potential of Vision Transformers (ViTs), known for their accuracy and interpretability, persists in this domain due to limitations in their 3D versions. This paper introduces Triamese-ViT, an innovative adaptation of the ViT model for brain age estimation. Our model uniquely combines ViTs from three different orientations to capture 3D information, significantly enhancing accuracy and interpretability. Tested on a dataset of 1351 MRI scans, Triamese-ViT achieves a Mean Absolute Error (MAE) of 3.84, a 0.9 Spearman correlation coefficient with chronological age, and a -0.29 Spearman correlation coefficient between the brain age gap (BAG) and chronological age, significantly better than previous methods for brian age estimation. A key innovation of Triamese-ViT is its capacity to generate a comprehensive 3D-like attention map, synthesized from 2D attention maps of each orientation-specific ViT. This feature is particularly beneficial for in-depth brain age analysis and disease diagnosis, offering deeper insights into brain health and the mechanisms of age-related neural changes.