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The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wide-field autofluorescence images of unlabelled tissue sections into images that are equivalent to the bright-field images of histologically stained versions of the same samples. A blind comparison, by board-certified pathologists, of this virtualdoi:10.1038/s41551-019-0362-y pmid:31142829 fatcat:zdykuiagvnehpndmg54x76gg4e