# Deep Learning-enabled Virtual Histological Staining of Biological Samples

arXiv: https://arxiv.org/abs/2211.06822 · HTML: https://mlsanity.com/paper/2211.06822/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 103 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue](https://mlsanity.com/paper/1803.11293/) (2018)
- [Mitosis domain generalization in histopathology images -- The MIDOG challenge](https://mlsanity.com/paper/2204.03742/) (2022)
- [BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix](https://mlsanity.com/paper/2204.11425/) (2022)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning](https://mlsanity.com/paper/1807.07701/) (2018)
- [Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue](https://mlsanity.com/paper/2001.07267/) (2020)
- [Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis](https://mlsanity.com/paper/2002.00647/) (2020)
- [Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning](https://mlsanity.com/paper/2112.05240/) (2021)
- [Cross-modality deep learning brings bright-field microscopy contrast to holography](https://mlsanity.com/paper/1811.07103/) (2018)
- [Virtual staining for mitosis detection in Breast Histopathology](https://mlsanity.com/paper/2003.07801/) (2020)
- [Virtual stain transfer in histology via cascaded deep neural networks](https://mlsanity.com/paper/2207.06578/) (2022)
- [Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks](https://mlsanity.com/paper/2207.02946/) (2022)
- [Virtual Histological Staining of Label-Free Total Absorption Photoacoustic Remote Sensing (TA-PARS)](https://mlsanity.com/paper/2203.02584/) (2022)
- [Deep learning enhanced mobile-phone microscopy](https://mlsanity.com/paper/1712.04139/) (2017)
- [Deep learning-based holographic polarization microscopy](https://mlsanity.com/paper/2007.00741/) (2020)
- [Self-Attentive Adversarial Stain Normalization](https://mlsanity.com/paper/1909.01963/) (2019)
- [Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning](https://mlsanity.com/paper/2112.08837/) (2021)

## Built on by (most cited first, 30 of 42)

- [Multistain Pretraining for Slide Representation Learning in Pathology](https://mlsanity.com/paper/2408.02859/) (2024)
- [Virtual histological staining of unlabeled autopsy tissue](https://mlsanity.com/paper/2308.00920/) (2023)
- [Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning](https://mlsanity.com/paper/2403.09100/) (2024)
- [Pixel super-resolved virtual staining of label-free tissue using diffusion models](https://mlsanity.com/paper/2410.20073/) (2024)
- [Digital staining in optical microscopy using deep learning -- a review](https://mlsanity.com/paper/2303.08140/) (2023)
- [Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization](https://mlsanity.com/paper/2304.04142/) (2023)
- [A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology](https://mlsanity.com/paper/2404.18458/) (2024)
- [GANs vs. Diffusion Models for virtual staining with the HER2match dataset](https://mlsanity.com/paper/2506.18484/) (2025)
- [IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusion](https://mlsanity.com/paper/2405.08197/) (2024)
- [Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images](https://mlsanity.com/paper/2407.00098/) (2024)
- [Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry](https://mlsanity.com/paper/2411.13120/) (2024)
- [From Traditional to Deep Learning Approaches in Whole Slide Image Registration: A Methodological Review](https://mlsanity.com/paper/2502.19123/) (2025)
- [Diffusion-based Virtual Staining from Polarimetric Mueller Matrix Imaging](https://mlsanity.com/paper/2503.01352/) (2025)
- [Patch-Wise Hypergraph Contrastive Learning with Dual Normal Distribution Weighting for Multi-Domain Stain Transfer](https://mlsanity.com/paper/2503.09523/) (2025)
- [Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data](https://mlsanity.com/paper/2504.09899/) (2025)
- [MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models](https://mlsanity.com/paper/2505.10294/) (2025)
- [Generative AI for Misalignment-Resistant Virtual Staining to Accelerate Histopathology Workflows](https://mlsanity.com/paper/2509.14119/) (2025)
- [Editable Stain Transformation Of Histological Images Using Unpaired GANs](https://mlsanity.com/paper/2312.03647/) (2023)
- [AI-driven projection tomography with multicore fibre-optic cell rotation](https://mlsanity.com/paper/2312.07631/) (2023)
- [Single color digital H&E staining with In-and-Out Net](https://mlsanity.com/paper/2405.13278/) (2024)
- [Integration of Programmable Diffraction with Digital Neural Networks](https://mlsanity.com/paper/2406.10688/) (2024)
- [Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining](https://mlsanity.com/paper/2409.05255/) (2024)
- [Cross-channel Perception Learning for H&E-to-IHC Virtual Staining](https://mlsanity.com/paper/2506.07559/) (2025)
- [Pathology-Guided Virtual Staining Metric for Evaluation and Training](https://mlsanity.com/paper/2507.12624/) (2025)
- [Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment](https://mlsanity.com/paper/2508.16209/) (2025)
- [Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining](https://mlsanity.com/paper/2601.02806/) (2026)
- [A Generative Foundation Model for Multimodal Histopathology](https://mlsanity.com/paper/2604.03635/) (2026)
- [Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining](https://mlsanity.com/paper/2306.00548/) (2023)
- [SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images](https://mlsanity.com/paper/2307.12138/) (2023)
- [Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology](https://mlsanity.com/paper/2407.06116/) (2024)

## Abstract (arXiv)

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.