The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
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Citations
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
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
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 1×
“Besides, some studies [21] propose using the non-local self-attention mechanism to model global dependencies.”
BackgroundZagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244
Attention U-Net: Learning Where to Look for the Pancreas
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.03999 · Cited by 5,458
Cited by
Swin-Unet: Unet-like pure transformer for medical image segmentation
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., & Wang, M. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-25066-8_9 · Cited by 5,640
UNETR: 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
“Recently, multiple methods were proposed that explore the possibility of using transformer-based models for the task of 2D image segmentation [52, 7, 41, 51].”
MethodUNETR: Transformers for 3D Medical Image Segmentation
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H., & Xu, D. (2021) · arXiv · doi:10.48550/arxiv.2103.10504 · Cited by 3,580
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
TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation
Chen, B., Liu, Y., Zhang, Z., Lu, G., & Kong, A. W. K. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3309626 · Cited by 459
Metadata
- Authors
- Zhang, Y., Liu, H., & Hu, Q.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 14-24
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3204166336
- Cited by
- 1,312
- References
- 32
- FWCI
- 63.93
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, Y., Liu, H., & Hu, Q. (2021). TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-87193-2_2
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