Corpus ID: 211482404

Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies

@article{Heinrich2020UnsupervisedLO,
  title={Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies},
  author={M. Heinrich and L. Hansen},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.14107}
}

M. Heinrich, L. Hansen

Published 2020

Computer Science, Engineering

ArXiv

Multimodal image registration is a very challenging problem for deep learning approaches. Most current work focuses on either supervised learning that requires labelled training scans and may yield models that bias towards annotated structures or unsupervised approaches that are based on hand-crafted similarity metrics and may therefore not outperform their classical non-trained counterparts. We believe that unsupervised domain adaptation can be beneficial in overcoming the current limitations… CONTINUE READING

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