The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer
Open accessAbstract
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 (Channel Transformer) 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.
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Cites
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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“2021), and for Synapse we set it to 24 following (Chen et al. 2021).”
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Cited by
VM-UNet: Vision Mamba UNet for Medical Image Segmentation
Ruan, J., Li, J., & Xiang, S. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3767748 · Cited by 804
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Metadata
- Authors
- Wang, H., Cao, P., Wang, J., & Zaiane, O. R.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2022-06-28
- Volume
- 36
- Issue
- 3
- Pages
- 2441-2449
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W3197957534
- Cited by
- 1,111
- References
- 13
- FWCI
- 91.36
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Wang, H., Cao, P., Wang, J., & Zaiane, O. R. (2022). UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v36i3.20144
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