# A Two-Stage Multi-Modal MRI Framework for Lifespan Brain Age Prediction

arXiv: https://arxiv.org/abs/2604.16655 · HTML: https://mlsanity.com/paper/2604.16655/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 17 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)
- [Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker](https://mlsanity.com/paper/1612.02572/) (2016)
- [Masked Image Modeling Advances 3D Medical Image Analysis](https://mlsanity.com/paper/2204.11716/) (2022)
- [An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset](https://mlsanity.com/paper/2010.15526/) (2020)
- [Detect and Correct Bias in Multi-Site Neuroimaging Datasets](https://mlsanity.com/paper/2002.05049/) (2020)
- [MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling](https://mlsanity.com/paper/2303.09373/) (2023)
- [Fetal Brain Tissue Annotation and Segmentation Challenge Results](https://mlsanity.com/paper/2204.09573/) (2022)
- [Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss](https://mlsanity.com/paper/2106.03052/) (2021)
- [Ordinal Classification with Distance Regularization for Robust Brain Age Prediction](https://mlsanity.com/paper/2403.10522/) (2023)

## Abstract (arXiv)

The accurate quantification of brain age from MRI has emerged as an important biomarker of brain health. However, existing approaches are often restricted to narrow age ranges and single-modality MRI data, limiting their capacity to capture the coordinated macro- and microstructural changes that unfold across the human lifespan. To address these limitations, we develop a multi-modal brain age framework to characterize the integrated evolution of brain morphology and white matter organization. Our model adopts a two-stage architecture, where modalities are processed independently and integrated via late fusion in both stages: first to estimate a probability distribution over six developmental stages, and then to predict age via probability-weighted stage-specialized experts. Experiments on nine datasets spanning fetal to elderly stages demonstrate competitive in-domain performance and out-of-domain generalization, with our method reducing MAE by 13% and 78% over existing baselines and multi-modal integration yielding 12-13% gains. Analysis of ADNI clinical groups further suggests the potential of the predicted brain age gap to characterize Alzheimer's-related brain aging.