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Medical image segmentation plays an essential role in developing computer-assisted diagnosis and therapy systems, yet still faces many challenges.
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G. Li and J. Kim, “Dabnet: Depth-wise asymmetric bottleneck for real-time semantic segmentation,” in 30th British Machine Vision Conference 2019, BMVC 2019, Cardiff, UK, September 9-12, 2019 . BMVA Press, 2019, p. 259. [Online]. Available: https://bmvc2019.org/wp-content/uploads/papers/0955-paper.pdf
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2021
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2021
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S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y. Wang, Y. Fu, J. Feng, T. Xiang, P. H. S. Torr, and L. Zhang, “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” 2021
2021
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2021
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H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmentation,” 2021
2021
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2021
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T. Wu, S. Tang, R. Zhang, J. Cao, and Y. Zhang, “Cgnet: A light-weight context guided network for semantic segmentation,” IEEE Transactions on Image Processing , vol. 30, pp. 1169–1179, 2021
2021
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