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• W2921322750 abstract "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 which is titled Cycle-MedGAN. 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." @default.
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• W2921322750 date "2019-09-01" @default.
• W2921322750 modified "2023-10-17" @default.
• W2921322750 title "Unsupervised Medical Image Translation Using Cycle-MedGAN" @default.
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• W2921322750 doi "https://doi.org/10.23919/eusipco.2019.8902799" @default.
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