解决脑影像诊断中亚型混杂与数据异质性问题，提升模型泛化能力。
A Federated Learning Framework for Handling Subtype Confounding and Heterogeneity in Large-Scale Neuroimaging Diagnosis
- 通过动态路由将样本分配给最匹配的局部模型，识别潜在疾病亚型。
- 跨站点平均准确率达74.06%，显著优于传统方法。
- 适合多中心神经精神疾病诊断研究，助力个性化医疗。
计算机辅助诊断（CAD）系统在神经系统和精神疾病脑影像分析中至关重要。然而，小样本研究可重复性差，而大规模数据集因多种疾病亚型被归为单一类别，导致混杂异质性。为此，我们提出一种专用于脑影像CAD的联邦学习框架，包含动态导航模块，根据潜在亚型表示将样本路由至最合适的本地模型；以及元整合模块，融合异构本地模型的预测结果生成统一诊断输出。我们在涵盖超过1300名重度抑郁症患者和1100名健康对照的多队列fMRI数据上评估该框架。实验表明，相比传统方法，本框架在诊断准确性和鲁棒性方面均有显著提升。其跨站点平均准确率达到74.06%，充分验证了处理亚型异质性的有效性，并增强了模型泛化能力。消融实验进一步证明动态导航与元整合模块对性能提升的关键作用。该框架有效缓解数据异质性与亚型混淆问题，推动可靠、可重复的脑影像辅助诊断系统发展，为神经病学与精神病学中的个性化医疗和临床决策提供重要支持。
原文摘要 · Abstract (English)
Computer-aided diagnosis (CAD) systems play a crucial role in analyzing neuroimaging data for neurological and psychiatric disorders. However, small-sample studies suffer from low reproducibility, while large-scale datasets introduce confounding heterogeneity due to multiple disease subtypes being labeled under a single category. To address these challenges, we propose a novel federated learning framework tailored for neuroimaging CAD systems. Our approach includes a dynamic navigation module that routes samples to the most suitable local models based on latent subtype representations, and a meta-integration module that combines predictions from heterogeneous local models into a unified diagnostic output. We evaluated our framework using a comprehensive dataset comprising fMRI data from over 1300 MDD patients and 1100 healthy controls across multiple study cohorts. Experimental results demonstrate significant improvements in diagnostic accuracy and robustness compared to traditional methods. Specifically, our framework achieved an average accuracy of 74.06\% across all tested sites, showcasing its effectiveness in handling subtype heterogeneity and enhancing model generalizability. Ablation studies further confirmed the importance of both the dynamic navigation and meta-integration modules in improving performance. By addressing data heterogeneity and subtype confounding, our framework advances reliable and reproducible neuroimaging CAD systems, offering significant potential for personalized medicine and clinical decision-making in neurology and psychiatry.
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