通过双优先级联邦预训练构建的通用结构脑MRI基础模型
A generalizable structural brain MRI foundation model built through dual-priority federated pretraining
- Peking University（北京大学）
- Peking University Health Science Center（北京大学医学部）
- Fudan University（复旦大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
BrainFedFM通过双优先级联邦预训练，在42个站点、164,707张扫描上构建结构脑MRI基础模型，在20个下游数据集上达到最先进性能，验证了联邦预训练在不汇集原始数据时开发神经影像基础模型的可行性。
中文摘要 AI 辅助
基础模型有望在发育、衰老和疾病过程中对结构脑磁共振成像（MRI）进行通用分析。然而，现有的模型通常通过对汇总数据进行集中预训练而构建，尽管存在隐私和治理约束。这种汇总优化可能过度强调队列规模，而忽视来自较小、专门队列的互补信息。在此，我们提出了BrainFedFM，一种结构脑MRI基础模型，该模型在来自多样真实世界数据分布的164,707张三维扫描上进行了联邦预训练，这些数据分布在42个联邦站点。BrainFedFM采用双优先级联邦预训练，将每个站点的空间优先级掩蔽与服务器的站点优先级聚合相结合，以在局部强调信息丰富的解剖区域，并在全局优先考虑站点贡献。在跨越17个分类、回归和分割任务的20个下游数据集上，BrainFedFM在七个模型中取得了最先进的性能（平均排名1.68，提升50%），包括四个集中式基础模型，同时在分类和回归任务中表现出特别一致的优越性，并在代表性不足的人群中表现出鲁棒性。这些发现证明了BrainFedFM的通用性，并强调联邦预训练是一种实用的策略，可以在不汇集原始图像的情况下，从分布式数据开发神经影像基础模型。
英文摘要
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.
