  Most state-of-the-art methods for medical image segmentation adopt the
encoder-decoder architecture. However, this U-shaped framework still has
limitations in capturing the non-local multi-scale information with a simple
skip connection. To solve the problem, we firstly explore the potential
weakness of skip connections in U-Net on multiple segmentation tasks, and find
that i) not all skip connections are useful, each skip connection has different
contribution; ii) the optimal combinations of skip connections are different,
relying on the specific datasets. Based on our findings, we propose a new
segmentation framework, named UDTransNet, to solve three semantic gaps in
U-Net. Specifically, we propose a Dual Attention Transformer (DAT) module for
capturing the channel- and spatial-wise relationships to better fuse the
encoder features, and a Decoder-guided Recalibration Attention (DRA) module for
effectively connecting the DAT tokens and the decoder features to eliminate the
inconsistency. Hence, both modules establish a learnable connection to solve
the semantic gaps between the encoder and the decoder, which leads to a
high-performance segmentation model for medical images. Comprehensive
experimental results indicate that our UDTransNet produces higher evaluation
scores and finer segmentation results with relatively fewer parameters over the
state-of-the-art segmentation methods on different public datasets. Code:
https://github.com/McGregorWwww/UDTransNet.
