AI RESEARCH
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
arXiv CS.CV
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ArXi:2604.11679v1 Announce Type: new Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obtain. Self-supervised learning (SSL) can address this by leveraging the vast amounts of unlabeled data produced in clinical workflows to train robust \textit{foundation models} that adapt out-of-domain with minimal supervision. However, the development of foundation models for brain MRI has been limited by small pre