Deep Learning-enabled Virtual Histological Staining of Biological Samples

Abstract

Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to as virtual staining, were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications.

35 Pages, 7 Figures, 2 Tables

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• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Virtual stain transfer in histology via cascaded deep neural networks
• Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks

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• Integration of Programmable Diffraction with Digital Neural Networks
• Automated Whole Slide Imaging for Label-Free Histology using Photon Absorption Remote Sensing Microscopy
• Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry
• Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining
• MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models
• Quantitative Stain Mapping in X-ray Virtual Histology
• GANs vs. Diffusion Models for virtual staining with the HER2match dataset