# Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue

arXiv: https://arxiv.org/abs/1803.11293 · HTML: https://mlsanity.com/paper/1803.11293/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

## Built on by (most cited first, 30 of 70)

- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [Multistain Pretraining for Slide Representation Learning in Pathology](https://mlsanity.com/paper/2408.02859/) (2024)
- [Uncertainty Quantification via Neural Posterior Principal Components](https://mlsanity.com/paper/2309.15533/) (2023)
- [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)
- [Hierarchical discriminative learning improves visual representations of biomedical microscopy](https://mlsanity.com/paper/2303.01605/) (2023)
- [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)
- [StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining](https://mlsanity.com/paper/2403.11340/) (2024)
- [Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon](https://mlsanity.com/paper/2308.13182/) (2023)
- [VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains](https://mlsanity.com/paper/2407.19113/) (2024)
- [Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study](https://mlsanity.com/paper/2103.16262/) (2021)
- [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)
- [Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning](https://mlsanity.com/paper/2112.08837/) (2021)
- [Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network](https://mlsanity.com/paper/2201.11333/) (2022)
- [Digital staining in optical microscopy using deep learning -- a review](https://mlsanity.com/paper/2303.08140/) (2023)
- [A comparative evaluation of image-to-image translation methods for stain transfer in histopathology](https://mlsanity.com/paper/2303.17009/) (2023)
- [HEMIT: H&E to Multiplex-immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator](https://mlsanity.com/paper/2403.18501/) (2024)
- [Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks](https://mlsanity.com/paper/2206.10152/) (2022)
- [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)
- [Hierarchical Uncertainty Exploration via Feedforward Posterior Trees](https://mlsanity.com/paper/2405.15719/) (2024)
- [A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections](https://mlsanity.com/paper/2108.10550/) (2021)
- [Image Translation Based Nuclei Segmentation for Immunohistochemistry Images](https://mlsanity.com/paper/2208.06202/) (2022)
- [Uncertainty Visualization via Low-Dimensional Posterior Projections](https://mlsanity.com/paper/2312.07804/) (2023)
- [AV-GAN: Attention-Based Varifocal Generative Adversarial Network for Uneven Medical Image Translation](https://mlsanity.com/paper/2404.10714/) (2024)
- [USIGAN: Unbalanced Self-Information Feature Transport for Weakly Paired Image IHC Virtual Staining](https://mlsanity.com/paper/2507.05843/) (2025)

## Abstract (arXiv)

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.