  Most recent semantic segmentation methods adopt a U-Net framework with an
encoder-decoder architecture. It is still challenging for U-Net with a simple
skip connection scheme to model the global multi-scale context: 1) Not each
skip connection setting is effective due to the issue of incompatible feature
sets of encoder and decoder stage, even some skip connection negatively
influence the segmentation performance; 2) The original U-Net is worse than the
one without any skip connection on some datasets. Based on our findings, we
propose a new segmentation framework, named UCTransNet (with a proposed CTrans
module in U-Net), from the channel perspective with attention mechanism.
Specifically, the CTrans module is an alternate of the U-Net skip connections,
which consists of a sub-module to conduct the multi-scale Channel Cross fusion
with Transformer (named CCT) and a sub-module Channel-wise Cross-Attention
(named CCA) to guide the fused multi-scale channel-wise information to
effectively connect to the decoder features for eliminating the ambiguity.
Hence, the proposed connection consisting of the CCT and CCA is able to replace
the original skip connection to solve the semantic gaps for an accurate
automatic medical image segmentation. The experimental results suggest that our
UCTransNet produces more precise segmentation performance and achieves
consistent improvements over the state-of-the-art for semantic segmentation
across different datasets and conventional architectures involving transformer
or U-shaped framework. Code: https://github.com/McGregorWwww/UCTransNet.
