Swin-Unet: Unet-like pure transformer for medical image segmentation
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Transformer/attention modules have been utilized in emerging neural architecture topologies, such as image segmentation, graph neural networks, graph convolutional networks, 3D convolutional neural networks, point clouds, and spiking transformer neural networks.
“Recently, transformer/attention modules have been utilized in emerging neural architecture topologies, such as image segmentation [262], graph neural networks (GNN) [263], graph convolutional network (GCN) [264], 3D convolutional neural networks [265], point clouds [266] and spiking transformer neural network [267].”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [262]
The base segmentation models used published predictions from Swin-UNETR and UNet.
“For the base segmentation models, we used the published predictions from transformer based Swin-UNETR [49] and UNet [2].”
From rNCA: Self-Repairing Segmentation Masks (2025) · cites this paper as [49]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534 · see citing works
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · cited 2× · see citing works
“Some studies have tried to address this problem by using atrous convolutional layers [10,11], self-attention mechanisms [12,13], and image pyramids [14].”
BackgroundPyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259 · cited 1× · see citing works
“Recently, several excellent works have been done baed on ViT [25,26,19].”
BackgroundTransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021) · arXiv · doi:10.48550/arxiv.2102.04306 · Cited by 4,206 · cited 8× · see citing works
“In particular, accurate and robust medical image segmentation can play a cornerstone role in computer-aided diagnosis and image-guided clinical surgery [1,2].”
BackgroundMethodInfluentialSelf-citationUNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., & Xu, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00181 · Cited by 3,586 · cited 3× · see citing works
“Currently, some efforts are being made to combine CNN and Transformer to break the dominance of CNNs in medical image segmentation [2,27,1].”
BackgroundMethod
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Semantic segmentation using Vision Transformers: A survey
Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., & Herath, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106669 · Cited by 288 · see citing works
A Survey of Techniques for Optimizing Transformer Inference
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154 · see citing works
A survey on deep learning in medical ultrasound imaging
Song, K., Feng, J., & Chen, D. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1398393 · Cited by 59 · see citing works
SwinAnomaly: Real-Time Video Anomaly Detection Using Video Swin Transformer and SORT
Bajgoti, A., Gupta, R., Balaji, P., Dwivedi, R., Siwach, M., & Gupta, D. (2023) · IEEE Access · doi:10.1109/access.2023.3321801 · Cited by 38 · see citing works
Zhong, F., He, K., Ji, M., Chen, J., Gao, T., Li, S., Zhang, J., & Li, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-59436-2 · Cited by 37 · see citing works
Self-citationDong, P., Zhuang, J., Yang, Z., Ji, S., Li, Y., Xu, D., Huang, H., Hu, J., Jones, A. K., Shi, Y., et al. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3443692 · Cited by 35 · see citing works
Self-citationO.K., S., Riera-Marín, M., Galdran, A., López, J. G., Rodríguez-Comas, J., Piella, G., & Ballester, M. A. G. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102547 · Cited by 11 · see citing works
Anatomically plausible segmentations: Explicitly preserving topology through prior deformations
Wyburd, M. K., Dinsdale, N. K., Jenkinson, M., & Namburete, A. I. L. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103222 · Cited by 11 · see citing works
Wang, R., Liu, Z., Pang, J., Sun, J., Xiang, M., & Ning, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3579060 · Cited by 9 · see citing works
rNCA: Self-Repairing Segmentation Masks
Silbernagel, M., Alonso, A., Petersen, J., Ibragimov, B., de Bruijne, M., & Wyburd, M. K. (2025) · arXiv · doi:10.48550/arxiv.2512.13397
“Baselines For the base segmentation models, we used the published predictions from transformer based Swin-UNETR [49] and UNet [2].”
Kanber, B. M., Gou, S., Liu, B., Noaman, N. F., Mukred, M., & Al Smadi, A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134426
- Authors
- Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M.
- Affiliations
- Technical University of Munich (DE)
- Fudan University (CN)
- Johns Hopkins University (US)
- Huawei Technologies (China) (CN)
- Corresponding author
- Jiang, Dongsheng
- Tian, Qi
- Wang, Manning
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 205-218
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4321232185
- Cited by
- 5,640
- References
- 8
- Citations by year
- 2026: 1101 · 2025: 1965 · 2024: 1497 · 2023: 845 · 2022: 214 · 2021: 18
- FWCI
- 578.36
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- Swin-Unet, medical image segmentation, Swin Transformer, hierarchical encoder-decoder, skip connections, patch expansion, multi-organ segmentation, cardiac image segmentation
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