UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer

TL;DR

UCTransNet introduces a channel-wise transformer-based skip connection mechanism in U-Net, improving global context modeling and segmentation accuracy in medical images by addressing the limitations of traditional skip connections.

Contribution

The paper proposes a novel CTrans module with transformer-based multi-scale channel fusion and cross-attention, replacing traditional skip connections in U-Net for better semantic gap bridging.

Findings

Achieves more precise segmentation across multiple datasets.

Consistently outperforms state-of-the-art methods.

Improves global multi-scale context modeling in U-Net.

Abstract

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.