GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning
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TL;DR - GenFAR is a modular brain-MRI representation framework trained on 49,246 participants across 11 cohorts and 17 clinical and biological tasks. Its reusable features improve accuracy and sample efficiency when training specialized downstream models.
- Sequential learning lets tasks progressively build on previously learned MRI representations.
- Testing 5,000 task sequences identified six tasks as the optimal sequence length.
- A new Donor Score highlighted Age, AD/MCI, MMSE, Hypertension, and Hyperlipidemia as strong contributors to downstream performance.
- The learned representation generalized to tasks outside training and supported more data-efficient secondary predictors.
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GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning
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TL;DR - GenFAR is a modular brain-MRI representation framework trained on 49,246 participants across 11 cohorts and 17 clinical and biological tasks. Its reusable features improve accuracy and sample efficiency when training specialized downstream models.
- Sequential learning lets tasks progressively build on previously learned MRI representations.
- Testing 5,000 task sequences identified six tasks as the optimal sequence length.
- A new Donor Score highlighted Age, AD/MCI, MMSE, Hypertension, and Hyperlipidemia as strong contributors to downstream performance.
- The learned representation generalized to tasks outside training and supported more data-efficient secondary predictors.