  Histological analysis of tissue samples is one of the most widely used
methods for disease diagnosis. After taking a sample from a patient, it goes
through a lengthy and laborious preparation, which stains the tissue to
visualize different histological features under a microscope. Here, we
demonstrate a label-free approach to create a virtually-stained microscopic
image using a single wide-field auto-fluorescence image of an unlabeled tissue
sample, bypassing the standard histochemical staining process, saving time and
cost. This method is based on deep learning, and uses a convolutional neural
network trained using a generative adversarial network model to transform an
auto-fluorescence image of an unlabeled tissue section into an image that is
equivalent to the bright-field image of the stained-version of the same sample.
We validated this method by successfully creating virtually-stained microscopic
images of human tissue samples, including sections of salivary gland, thyroid,
kidney, liver and lung tissue, also covering three different stains. This
label-free virtual-staining method eliminates cumbersome and costly
histochemical staining procedures, and would significantly simplify tissue
preparation in pathology and histology fields.
