/RAUNet

RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments (ICONIP19)

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RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments (ICONIP19)

Paper address: https://link.springer.com/chapter/10.1007/978-3-030-36711-4_13

arxiv: https://arxiv.org/abs/1909.10360

Zhen-Liang Ni, Gui-Bin Bian, Xiao-Hu Zhou, Zeng-Guang Hou, Xiao-Liang Xie, Chen Wang, Yan-Jie Zhou, Rui-Qi Li, Zhen Li

Chinese introduction： https://blog.csdn.net/big_dreamer1/article/details/101228624

Note: The size of the input image should be divisible by 32.

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Citation

If you find RAUNet useful in your research, please consider citing:

@inproceedings{ni2019raunet,
  title={RAUNet: Residual attention U-Net for semantic segmentation of cataract surgical instruments},
  author={Ni, Zhen-Liang and Bian, Gui-Bin and Zhou, Xiao-Hu and Hou, Zeng-Guang and Xie, Xiao-Liang and Wang, Chen and Zhou, Yan-Jie and Li, Rui-Qi and Li, Zhen},
  booktitle={International Conference on Neural Information Processing},
  pages={139--149},
  year={2019},
  organization={Springer}
}