AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation

@article{Yan2022AFTerUNetAF,
  title={AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation},
  author={Xiangyi Yan and Hao Tang and Shanlin Sun and Haoyu Ma and Deying Kong and Xiaohui Xie},
  journal={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2022},
  pages={3270-3280}
}
  • Xiangyi Yan, Hao Tang, Xiaohui Xie
  • Published 20 October 2021
  • Computer Science
  • 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Recent advances in transformer-based models have drawn attention to exploring these techniques in medical image segmentation, especially in conjunction with the UNet model (or its variants), which has shown great success in medical image segmentation, under both 2D and 3D settings. Current 2D based methods either directly replace convolutional layers with pure transformers or consider a transformer as an additional intermediate encoder between the encoder and decoder of U-Net. However, these… 

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