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.

本研究使用基于深度学习的生成对抗网络模型，将未标记组织样本的自动荧光图像转化为等效于其染色样本的亮场图像，实现了一种免标记虚拟染色的显微镜图像制作方法，有效节约了组织学检查的时间和成本。