Bridging the Gap Between Paired and Unpaired Medical Image Translation

@article{Paavilainen2021BridgingTG,
  title={Bridging the Gap Between Paired and Unpaired Medical Image Translation},
  author={Pauliina Paavilainen and Saad Ullah Akram and Juho Kannala},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.08407}
}
Medical image translation has the potential to reduce the imaging workload, by removing the need to capture some sequences, and to reduce the annotation burden for developing machine learning methods. GANs have been used successfully to translate images from one domain to another, such as MR to CT. At present, paired data (registered MR and CT images) or extra supervision (e.g. segmentation masks) is needed to learn good translation models. Registering multiple modalities or annotating… 

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