规范化(社会学)
计算机科学
箱子
分割
人工智能
领域(数学分析)
一般化
相似性(几何)
块(置换群论)
数据挖掘
图像(数学)
模式识别(心理学)
机器学习
算法
数学
社会学
数学分析
几何学
人类学
作者
Fubao Zhu,Yazhou Tian,Chunlei Han,Yanting Li,Jiaofen Nan,Yizhao Ni,Weihua Zhou
出处
期刊:Cornell University - arXiv
日期:2023-06-29
标识
DOI:10.48550/arxiv.2306.17008
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.
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