# A generalizable structural brain MRI foundation model built through dual-priority federated pretraining

arXiv: https://arxiv.org/abs/2609.27611 · HTML: https://mlsanity.com/paper/2609.27611/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 55 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [Revisiting MAE pre-training for 3D medical image segmentation](https://mlsanity.com/paper/2410.23132/) (2024)
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- [An OpenMind for 3D medical vision self-supervised learning](https://mlsanity.com/paper/2412.17041/) (2024)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning](https://mlsanity.com/paper/2411.16380/) (2024)
- [Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks](https://mlsanity.com/paper/2308.07003/) (2023)
- [Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets](https://mlsanity.com/paper/2211.04125/) (2022)
- [The Imaging Database for Epilepsy And Surgery (IDEAS)](https://mlsanity.com/paper/2406.06731/) (2024)
- [PediDemi -- A Pediatric Demyelinating Lesion Segmentation Dataset](https://mlsanity.com/paper/2508.13239/) (2025)
- [From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?](https://mlsanity.com/paper/2509.17280/) (2025)
- [Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data](https://mlsanity.com/paper/2607.02447/) (2026)

## Abstract (arXiv)

Foundation models hold promise for generalizable analysis of structural brain magnetic resonance imaging (MRI) across development, aging and disease. However, existing models are typically built through centralized pretraining on pooled data, despite privacy and governance constraints. Such pooling optimization can overemphasize cohort size and overlook complementary information from smaller, specialized cohorts. Here we present BrainFedFM, a structural brain MRI foundation model federatively pretrained on 164,707 three-dimensional scans drawn from diverse real-world data distributions and organized across 42 federated sites. BrainFedFM uses dual-priority federated pretraining, coupling spatial-priority masking at each site with site-priority aggregation at the server to emphasize informative anatomical regions locally and prioritize site contributions globally. Across 20 downstream datasets spanning 17 classification, regression and segmentation tasks, BrainFedFM achieved the state-of-the-art performance (mean rank 1.68, 50% gain) across seven models, including four centralized foundation models, while showing particularly consistent advantages in classification and regression and robustness across underrepresented populations. These findings demonstrate the generalizability of BrainFedFM and highlight federated pretraining as a practical strategy for developing neuroimaging foundation models from distributed data without pooling raw images.