Federated learning is an emerging paradigm allowing large-scale decentralized learning without sharing data across different data owners, which helps address the concern of data privacy in medical image analysis. However, the requirement for label consistency across clients by the existing methods largely narrows its application scope. In practice, each clinical site may only annotate certain organs of interest with partial or no overlap with other sites. Incorporating such partially labeled data into a unified federation is an unexplored problem with clinical significance and urgency. This work tackles the challenge by using a novel federated multi-encoding U-Net (Fed-MENU) method for multi-organ segmentation. In our method, a multi-encoding U-Net (MENU-Net) is proposed to extract organ-specific features through different encoding sub-networks. Each sub-network can be seen as an expert of a specific organ and trained for that client. Moreover, to encourage the organ-specific features extracted by different sub-networks to be informative and distinctive, we regularize the training of the MENU-Net by designing an auxiliary generic decoder (AGD). Extensive experiments on six public abdominal CT datasets show that our Fed-MENU method can effectively obtain a federated learning model using the partially labeled datasets with superior performance to other models trained by either localized or centralized learning methods. Source code is publicly available at https://github.com/DIAL-RPI/Fed-MENU.

翻译：摘要：联邦学习是一种新兴范式，可在不跨数据拥有者共享数据的情况下实现大规模分散式学习，有助于解决医学图像分析中的数据隐私问题。然而，现有方法对跨客户端标签一致性的要求极大地限制了其应用范围。在实践中，每个临床站点可能仅标注某些感兴趣的器官，且标注内容与其他站点部分重叠或完全不重叠。将这些部分标注数据整合到统一联邦中是一个尚未探索的、具有临床意义和紧迫性的问题。本研究通过提出一种新颖的联邦多编码U-Net（Fed-MENU）方法来解决这一挑战，用于多器官分割。在我们的方法中，提出了一种多编码U-Net（MENU-Net），通过不同的编码子网络提取器官特异性特征。每个子网络可被视为特定器官的专家，并针对该客户端进行训练。此外，为了促进不同子网络提取的器官特异性特征具有信息性和区分性，我们通过设计一个辅助通用解码器（AGD）来正则化MENU-Net的训练。在六个公开腹部CT数据集上的大量实验表明，我们的Fed-MENU方法能够有效利用部分标注数据集获得联邦学习模型，其性能优于其他通过局部或集中式学习方法训练的模型。源代码公开于https://github.com/DIAL-RPI/Fed-MENU。