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Unsupervised Medical Image Translation Using Cycle-MedGAN
[article]
2019
arXiv
pre-print
Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. However, for supervised image translation frameworks, co-registered datasets, paired in a pixel-wise sense, are required. This is often difficult to acquire in realistic medical scenarios. On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. In this work, we propose a new unsupervised translation framework
arXiv:1903.03374v1
fatcat:6eittos3tjhbrddjdi4optojqu