A generalizable structural brain MRI foundation model built through dual-priority federated pretraining
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TL;DR - BrainFedFM is a structural brain MRI foundation model pretrained federatively on 164,707 scans across 42 sites, avoiding the need to pool sensitive raw images. Its dual-priority training approach achieved leading average performance across diverse neuroimaging tasks and improved robustness for underrepresented populations.
- Spatial-priority masking emphasizes informative anatomical regions during local site training.
- Site-priority server aggregation weights complementary contributions beyond cohort size alone.
- Evaluation covered 20 datasets and 17 classification, regression, and segmentation tasks.
- BrainFedFM ranked first on average among seven models, including four centralized foundation models, with especially consistent gains in classification and regression.
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A generalizable structural brain MRI foundation model built through dual-priority federated pretraining
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TL;DR - BrainFedFM is a structural brain MRI foundation model pretrained federatively on 164,707 scans across 42 sites, avoiding the need to pool sensitive raw images. Its dual-priority training approach achieved leading average performance across diverse neuroimaging tasks and improved robustness for underrepresented populations.
- Spatial-priority masking emphasizes informative anatomical regions during local site training.
- Site-priority server aggregation weights complementary contributions beyond cohort size alone.
- Evaluation covered 20 datasets and 17 classification, regression, and segmentation tasks.
- BrainFedFM ranked first on average among seven models, including four centralized foundation models, with especially consistent gains in classification and regression.