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The CNN-based methods have achieved impressive results in medical image segmentation, but they failed to capture the long-range dependencies due to the inherent locality of the convolution operation.
How much position information do convolutional neural networks encode?
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An image is worth 16x16 words: Transformers for image recognition at scale
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Fully convolutional networks for semantic segmentation
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Pyramid scene parsing network
Zhao, H.; Shi, J.; Qi, X.; Wang, X.; and Jia, J. 2017 · 2017
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Reverse attention for salient object detection
Chen, S.; Tan, X.; Wang, B.; and Hu, X. 2018 · 2018
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Squeeze-and-excitation networks
Hu, J.; Shen, L.; and Sun, G. 2018 · 2018
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Unet++: A nested u-net architecture for medical image segmentation
Zhou, Z.; Siddiquee, M. M. R.; Tajbakhsh, N.; and Liang, J. 2018 · 2018
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Ce-net: Context encoder network for 2d medical image segmentation
Gu, Z.; Cheng, J.; Fu, H.; Zhou, K.; Hao, H.; Zhao, Y.; Zhang, T.; Gao, S.; and Liu, J. 2019 · 2019
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CS-Net: channel and spatial attention network for curvilinear structure segmentation
Mou, L.; Zhao, Y.; Chen, L.; Cheng, J.; Gu, Z.; Hao, H.; Qi, H.; Zheng, Y.; Frangi, A.; and Liu, J. 2019 · 2019
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Et-net: A generic edge-attention guidance network for medical image segmentation
Zhang, Z.; Fu, H.; Dai, H.; Shen, J.; Pang, Y.; and Shao, L. 2019 · 2019
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End-to-end object detection with transformers
Multi-scale self-guided attention for medical image segmentation
Sinha, A.; and Dolz, J. 2020 · 2020
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Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
Cao, H.; Wang, Y.; Chen, J.; Jiang, D.; Zhang, X.; Tian, Q.; and Wang, M. 2021 · 2021
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Transunet: 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.; and Zhou, Y. 2021 · 2021
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LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference
Graham, B.; El-Nouby, A.; Touvron, H.; Stock, P.; Joulin, A.; Jégou, H.; and Douze, M. 2021 · 2021
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Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
Cited alongside, same era.
Pranet: Parallel reverse attention network for polyp segmentation
Fan, D.-P.; Ji, G.-P.; Zhou, T.; Chen, G.; Fu, H.; Shen, J.; and Shao, L. 2020 · 2020
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CPFNet: Context pyramid fusion network for medical image segmentation
Feng, S.; Zhao, H.; Shi, F.; Cheng, X.; Wang, M.; Ma, Y.; Xiang, D.; Zhu, W.; and Chen, X. 2020 · 2020
Cited alongside, same era.
Unet 3+: A full-scale connected unet for medical image segmentation
Huang, H.; Lin, L.; Tong, R.; Hu, H.; Zhang, Q.; Iwamoto, Y.; Han, X.; Chen, Y.-W.; and Wu, J. 2020 · 2020
Cited alongside, same era.
Rethinking skip connection with layer normalization
Liu, F.; Ren, X.; Zhang, Z.; Sun, X.; and Zou, Y. 2020 · 2020
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Twins: Revisiting the design of spatial attention in vision transformers
Chu, X.; Tian, Z.; Wang, Y.; Zhang, B.; Ren, H.; Wei, X.; Xia, H.; and Shen, C. 2021a
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Conditional positional encodings for vision transformers
Chu, X.; Tian, Z.; Zhang, B.; Wang, X.; Wei, X.; Xia, H.; and Shen, C. 2021b
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Hatamizadeh, A.; Yang, D.; Roth, H.; and Xu, D. 2021 · 2021
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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F.; Jaeger, P. F.; Kohl, S. A.; Petersen, J.; and Maier-Hein, K. H. 2021 · 2021
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Localvit: Bringing locality to vision transformers
Li, Y.; Zhang, K.; Cao, J.; Timofte, R.; and Van Gool, L. 2021 · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
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Training data-efficient image transformers & distillation through attention
Touvron, H.; Cord, M.; Douze, M.; Massa, F.; Sablayrolles, A.; and Jégou, H. 2021 · 2021
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Zheng, S.; Lu, J.; Zhao, H.; Zhu, X.; Luo, Z.; Wang, Y.; Fu, Y.; Feng, J.; Xiang, T.; Torr, P. H.; et al. 2021 · 2021
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