Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification

• URL: http://arxiv.org/abs/2508.21458v1
• Date: Fri, 29 Aug 2025 09:43:02 GMT
• Title: Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification
• Authors: Kaouther Mouheb, Marawan Elbatel, Janne Papma, Geert Jan Biessels, Jurgen Claassen, Huub Middelkoop, Barbara van Munster, Wiesje van der Flier, Inez Ramakers, Stefan Klein, Esther E. Bron,
• Abstract summary: We evaluate the impact of key design choices on foundation models (FMs) tuning using brain MRI data.<n>We find that the architecture of the classification head substantially influences performance.<n>We highlight trade-offs that should guide future method development.
• Score: 0.8825314772327646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While foundation models (FMs) offer strong potential for AI-based dementia diagnosis, their integration into federated learning (FL) systems remains underexplored. In this benchmarking study, we systematically evaluate the impact of key design choices: classification head architecture, fine-tuning strategy, and aggregation method, on the performance and efficiency of federated FM tuning using brain MRI data. Using a large multi-cohort dataset, we find that the architecture of the classification head substantially influences performance, freezing the FM encoder achieves comparable results to full fine-tuning, and advanced aggregation methods outperform standard federated averaging. Our results offer practical insights for deploying FMs in decentralized clinical settings and highlight trade-offs that should guide future method development.

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