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We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images. Our proposed method performs Image Modality Translation (abbreviated as IMT) by means of a deep learning model that leverages conditional generative adversarial networks (cGANs). Our framework jointly exploits the low-level features (pixel-wise information) and high-level representations (e.g. brain tumors, brain structure like gray matter, etc.) between crossdoi:10.1038/s41598-020-60520-6 pmid:32111966 pmcid:PMC7048849 fatcat:gimqzl7w2vfnjf4iisypuig2kq