发表机构
United International University（联合国际大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出联邦物理信息神经网络，结合联邦学习与线性弹性方程的物理约束，在保护数据隐私的前提下，实现了脑肿瘤生物力学建模，其性能优于汇集数据训练的基线模型。
AI 中文摘要
胶质瘤、脑膜瘤和垂体腺瘤等脑肿瘤会改变柔软脑组织的力学行为，但常见的诊断方法依赖静态成像，无法捕捉肿瘤生长、组织位移或刚度随时间的变化。针对该任务的深度学习模型通常需要将患者数据汇集到同一站点，这与GDPR、HIPAA等隐私规则冲突，且限制了跨机构的泛化能力，鉴于患者的多样性，这一挑战在神经肿瘤学中尤为突出。本研究提出一种联邦物理信息神经网络，将联邦学习与基于线性弹性方程构建的物理信息损失相结合。三个模拟临床站点利用患者特定的MRI数据，通过物理信息损失训练本地网络，仅通过FedAvg协议在100轮训练中向中央服务器共享模型权重，原始数据保留在其来源站点。该联邦模型的总体准确率达到91.4%，而基于汇集数据训练的非联邦基线模型准确率为90.0%；各类肿瘤的平均AUC为0.985，垂体肿瘤的准确率从85.6%提升至94.5%。训练生成的平滑、无散度位移场与预期的组织变形一致，表明联邦训练可与基于物理的约束相结合，且不会出现显著的性能损失。
英文摘要
Brain tumors such as glioma, meningioma, and pituitary adenoma alter the mechanical behavior of soft brain tissue, yet common diagnostic methods rely on static imaging that cannot capture tumor growth, tissue displacement, or changes in stiffness over time. Deep learning models for this task typically require pooling patient data at one site, which conflicts with privacy rules such as GDPR and HIPAA and limits generalization across institutions, a challenge that is pronounced in neuro oncology given patient diversity. This study presents a federated physics informed neural network combining federated learning with a physics informed loss built on the equations of linear elasticity. Three simulated clinical sites each train a local network on patient specific MRI data using a physics informed loss, and only model weights are shared with a central server through the FedAvg protocol over one hundred rounds, keeping raw data at its site of origin. The federated model reached an overall accuracy of 91.4%, against 90.0% for a non federated baseline trained on pooled data, an average AUC of 0.985 across tumor classes, and a rise in pituitary tumor accuracy from 85.6 to 94.5%. Training produced smooth, divergence free displacement fields consistent with expected tissue deformation, showing that federated training can be paired with physics based constraints without a meaningful loss in performance.
Comments11 pages, 9 figures
