Unsupervised Medical Image Translation Using Cycle-MedGAN

@article{Armanious2019UnsupervisedMI,
  title={Unsupervised Medical Image Translation Using Cycle-MedGAN},
  author={Karim Armanious and Chenming Jiang and Sherif Abdulatif and Thomas K{\"u}stner and S. Gatidis and Bin Yang},
  journal={2019 27th European Signal Processing Conference (EUSIPCO)},
  year={2019},
  pages={1-5}
}
Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. [...] Key Method The proposed framework utilizes new non-adversarial cycle losses which direct the framework to minimize the textural and perceptual discrepancies in the translated images. Qualitative and quantitative comparisons against other unsupervised translation approaches demonstrate the performance of the proposed framework for PET-CT translation and MR motion correction.Expand
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