  Although convolutional neural networks (CNNs) are promoting the development
of medical image semantic segmentation, the standard model still has some
shortcomings. First, the feature mapping from the encoder and decoder
sub-networks in the skip connection operation has a large semantic difference.
Second, the remote feature dependence is not effectively modeled. Third, the
global context information of different scales is ignored. In this paper, we
try to eliminate semantic ambiguity in skip connection operations by adding
attention gates (AGs), and use attention mechanisms to combine local features
with their corresponding global dependencies, explicitly model the dependencies
between channels and use multi-scale predictive fusion to utilize global
information at different scales. Compared with other state-of-the-art
segmentation networks, our model obtains better segmentation performance while
introducing fewer parameters.
