The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation
Open accessAbstract
Automatic medical image segmentation has made great progress owing to the powerful deep representation learning. Inspired by the success of self-attention mechanism in Transformer, considerable efforts are devoted to designing the robust variants of encoder-decoder architecture with Transformer. However, the patch division used in the existing Transformer-based models usually ignores the pixel-level intrinsic structural features inside each patch. In this paper, we propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet), which aims to incorporate the hierarchical Swin Transformer into both encoder and decoder of the standard U-shaped architecture. Our DS-TransUNet benefits from the self-attention computation in Swin Transformer and the designed dual-scale encoding, which can effectively model the non-local dependencies and multi-scale contexts for enhancing the semantic segmentation quality of varying medical images. Unlike many prior Transformer-based solutions, the proposed DS-TransUNet adopts a well-established dual-scale encoding mechanism that utilizes dual-scale encoders based on Swin Transformer to extract the coarse and fine-grained feature representations of different semantic scales. Meanwhile, a well-designed Transformer Interactive Fusion (TIF) module is proposed to effectively perform the multi-scale information fusion through the self-attention mechanism. Furthermore, we introduce the Swin Transformer block into decoder to further explore the long-range contextual information during the up-sampling process. Extensive experiments across four typical tasks for medical image segmentation demonstrate the effectiveness of DS-TransUNet, and our approach significantly outperforms the state-of-the-art methods.
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Cites
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Self-citationFeature Pyramid Networks for Object Detection
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“There exists some works trying to model long-range dependencies for convolution such as attention mechanism [9] [10] [11].”
Background
Cited by
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Tang, Y., Yang, D., Li, W., Roth, H. R., Landman, B., Xu, D., Nath, V., & Hatamizadeh, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02007 · Cited by 849
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
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
U-Net-Based Medical Image Segmentation
Yin, X.-X., Sun, L., Fu, Y., Lu, R., & Zhang, Y. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4189781 · Cited by 318
RetractedDilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound
Wu, Y., Yang, Y., Zhu, L., Han, Z., Luo, H., Xue, X., & Wang, W. (2023) · Frontiers in Physics · doi:10.3389/fphy.2023.1239400 · Cited by 2
Metadata
- Authors
- Lin, A., Chen, B., Xu, J., Zhang, Z., Lu, G., & Zhang, D.
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2022
- Volume
- 71
- Pages
- 1-15
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W3168491317
- Cited by
- 882
- References
- 22
- FWCI
- 64.99
- Type
- journal article
- Language
- en
- Open access
- green
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