MLA-BIN: Model-level Attention and Batch-instance Style Normalization
  for Domain Generalization of Federated Learning on Medical Image Segmentation
Fubao Zhu Yanhui Tian Chuang Han Yanting Li Jiaofen Nan Ni Yao Weihua Zhou
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.