Abstract:BackgroundU‐Net and its variations have achieved remarkable performances in medical image segmentation. However, they have two limitations. First, the shallow layer feature of the encoder always contains background noise. Second, semantic gaps exist between the features of the encoder and the decoder. Skip‐connections directly connect the encoder to the decoder, which will lead to the fusion of semantically dissimilar feature maps.PurposeTo overcome these two limitations, this paper proposes a novel medical im… Show more
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