Corpus ID: 236318367

AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training

@article{Yao2021ADGANEU,
  title={AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training},
  author={Kai Yao and Kaizhu Huang and Jie Sun and Curran Jude},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.11022}
}
  • Kai Yao, Kaizhu Huang, +1 author Curran Jude
  • Published 23 July 2021
  • Computer Science, Engineering
  • ArXiv
We consider unsupervised cell nuclei segmentation in this paper. Exploiting the recently-proposed unpaired image-to-image translation between cell nuclei images and randomly synthetic masks, existing approaches, e.g., CycleGAN, have achieved encouraging results. However, these methods usually take a two-stage pipeline and fail to learn end-to-end in cell nuclei images. More seriously, they could lead to the lossy transformation problem, i.e., the content inconsistency between the original… Expand

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