Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Department of Radiology, Lucas Center, Stanford University, Stanford, CA, USA.
Nat Commun. 2024 Sep 2;15(1):7615. doi: 10.1038/s41467-024-51172-5.
While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance on a diverse, realistic, multi-center cohort. Pediatric brain tumors were targeted due to the scarcity of such datasets, even in tertiary care hospitals. Our platform orchestrates federated training for joint tumor classification and segmentation across 19 international sites. FL-PedBrain exhibits less than a 1.5% decrease in classification and a 3% reduction in segmentation performance compared to centralized data training. FL boosts segmentation performance by 20 to 30% on three external, out-of-network sites. Finally, we explore the sources of data heterogeneity and examine FL robustness in real-world scenarios with data imbalances.
尽管有多种因素会影响疾病，但由于数据共享和隐私问题，医学中的人工智能 (AI) 研究通常使用规模较小且不具有多样性的患者队列。联邦学习 (FL) 的出现提供了一种解决方案，可以在不直接共享数据的情况下在多家医院进行训练。在这里，我们展示了用于儿童后颅窝脑肿瘤的 FL-PedBrain，评估了其在多样化、真实、多中心队列上的性能。选择儿童脑肿瘤是因为即使在三级护理医院，此类数据集也非常稀缺。我们的平台协调了来自 19 个国际站点的联合肿瘤分类和分割的联邦训练。与集中式数据训练相比，FL-PedBrain 的分类性能下降不到 1.5%，分割性能下降 3%。FL 可将三个外部、非网络站点的分割性能提高 20% 至 30%。最后，我们探讨了数据异质性的来源，并在存在数据不平衡的真实场景中检查了 FL 的稳健性。
