MLA-BIN：用于医学图像分割联邦学习领域泛化的模型级注意力与批实例风格归一化
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation
AI总结：
针对联邦学习在医学图像分割未见域上的性能下降问题，提出MLA-BIN方法，通过设计模型级注意力模块和批实例风格归一化块，有效提升模型的领域泛化能力，并在实验中优于现有方法。
摘要：
联邦学习（FL）的隐私保护机制为跨中心医疗协作与数据共享提供了有效解决方案。在多中心医学图像分割中，每个医疗站点作为FL的一个客户端，其数据自然形成一个域。FL提供了提升已见域模型性能的可能性。然而，在实际部署中存在领域泛化（DG）问题，即由FL训练的模型在未见域上的性能会下降。因此，本研究提出MLA-BIN来解决FL的DG问题。具体而言，设计了模型级注意力模块（MLA）和批实例风格归一化（BIN）块。MLA将未见域表示为已见域模型的线性组合。引入注意力机制作为加权系数，根据域间数据特征的相似性获取最优系数。MLA使全局模型能够泛化到未见域。在BIN块中，将批归一化（BN）和实例归一化（IN）相结合，对分割网络的浅层进行风格归一化，解决域间图像风格差异对DG的影响。两个医学图像分割任务的大量实验结果表明，所提出的MLA-BIN优于最先进的方法。
英文摘要：
The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a client of FL, and its data naturally forms a domain. FL supplies the possibility to improve the performance of seen domains model. However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease. Hence, MLA-BIN is proposed to solve the DG of FL in this study. Specifically, the model-level attention module (MLA) and batch-instance style normalization (BIN) block were designed. The MLA represents the unseen domain as a linear combination of seen domain models. The atten-tion mechanism is introduced for the weighting coefficient to obtain the optimal coefficient ac-cording to the similarity of inter-domain data features. MLA enables the global model to gen-eralize to unseen domain. In the BIN block, batch normalization (BN) and instance normalization (IN) are combined to perform the shallow layers of the segmentation network for style normali-zation, solving the influence of inter-domain image style differences on DG. The extensive experimental results of two medical image seg-mentation tasks demonstrate that the proposed MLA-BIN outperforms state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
