Corpus ID: 49322589

Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT

@article{Welander2018GenerativeAN,
  title={Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT},
  author={Per Welander and Simon Karlsson and Anders Eklund},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.07777}
}
  • Per Welander, Simon Karlsson, Anders Eklund
  • Published 2018
  • Computer Science
  • ArXiv
  • In medical imaging, a general problem is that it is costly and time consuming to collect high quality data from healthy and diseased subjects. Generative adversarial networks (GANs) is a deep learning method that has been developed for synthesizing data. GANs can thereby be used to generate more realistic training data, to improve classification performance of machine learning algorithms. Another application of GANs is image-to-image translations, e.g. generating magnetic resonance (MR) images… CONTINUE READING

    Citations

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    Medical Image Synthesis via Deep Learning.

    Unsupervised Medical Image Translation Using Cycle-MedGAN

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    Generative Adversarial Network in Medical Imaging: A Review

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    DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

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