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METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy
2021
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research. Yet, a massive lack of annotations prevents the broad use of deep learning to analyze such data. In this paper, we introduce a novel generative method which leverages real anatomical information to generate realistic image-label pairs of tumours. We construct a dualpathway generator, for the anatomical image and label, trained in a cycle-consistent setup, constrained by
doi:10.5167/uzh-220112
fatcat:afrvuhdnmrf7hedj2q2juutb7i