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Histological analysis of tissue samples is one of the most widely usedmethods for disease diagnosis. After taking a sample from a patient, it goesthrough a lengthy and laborious preparation, which stains the tissue tovisualize different histological features under a microscope. Here, wedemonstrate a label-free approach to create a virtually-stained microscopicimage using a single wide-field auto-fluorescence image of an unlabeled tissuesample, bypassing the standard histochemical staining process, saving time andcost. This method is based on deep learning, and uses a convolutional neuralnetwork trained using a generative adversarial network model to transform anauto-fluorescence image of an unlabeled tissue section into an image that isequivalent to the bright-field image of the stained-version of the same sample.We validated this method by successfully creating virtually-stained microscopicimages of human tissue samples, including sections of salivary gland, thyroid,kidney, liver and lung tissue, also covering three different stains. Thislabel-free virtual-staining method eliminates cumbersome and costlyhistochemical staining procedures, and would significantly simplify tissuepreparation in pathology and histology fields.

Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue