# Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

arXiv: https://arxiv.org/abs/2001.07267 · HTML: https://mlsanity.com/paper/2001.07267/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2020.

## Built on by (most cited first, 29 of 29)

- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning](https://mlsanity.com/paper/2112.05240/) (2021)
- [Self-supervised learning of hologram reconstruction using physics consistency](https://mlsanity.com/paper/2209.08288/) (2022)
- [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)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [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)
- [A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology](https://mlsanity.com/paper/2404.18458/) (2024)
- [Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion](https://mlsanity.com/paper/2412.11106/) (2024)
- [Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems](https://mlsanity.com/paper/2305.12852/) (2023)
- [Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging](https://mlsanity.com/paper/2109.09004/) (2021)
- [Image Translation Based Nuclei Segmentation for Immunohistochemistry Images](https://mlsanity.com/paper/2208.06202/) (2022)
- [Automated Whole Slide Imaging for Label-Free Histology using Photon Absorption Remote Sensing Microscopy](https://mlsanity.com/paper/2304.13736/) (2023)
- [Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry](https://mlsanity.com/paper/2411.13120/) (2024)
- [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)
- [HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks](https://mlsanity.com/paper/2210.06909/) (2022)
- [Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation](https://mlsanity.com/paper/2303.02057/) (2023)
- [Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining](https://mlsanity.com/paper/2409.05255/) (2024)
- [Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment](https://mlsanity.com/paper/2508.16209/) (2025)
- [Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology](https://mlsanity.com/paper/2407.06116/) (2024)
- [Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning](https://mlsanity.com/paper/2407.12337/) (2024)
- [VLM-based Prompts as the Optimal Assistant for Unpaired Histopathology Virtual Staining](https://mlsanity.com/paper/2504.15545/) (2025)
- [Detecting immune cells with label-free two-photon autofluorescence and deep learning](https://mlsanity.com/paper/2506.14449/) (2025)
- [Autonomous labeling of surgical resection margins using a foundation model](https://mlsanity.com/paper/2511.22131/) (2025)

## Abstract (arXiv)

Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time-consuming, labor-intensive, expensive and destructive to the specimen. Recently, the ability to virtually-stain unlabeled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain specific deep neural networks. Here, we present a new deep learning-based framework which generates virtually-stained images using label-free tissue, where different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabeled kidney tissue sections to generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones silver stain, and Masson's Trichrome stain. Using a single network, this approach multiplexes virtual staining of label-free tissue with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created on the same tissue cross-section, which is currently not feasible with standard histochemical staining methods.