# Virtual stain transfer in histology via cascaded deep neural networks arXiv: https://arxiv.org/abs/2207.06578 ยท HTML: https://mlsanity.com/paper/2207.06578/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 41 references that are in the map. - First posted on arXiv 14 Jul 2022. ## Builds on (most cited first, 16 of 16) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [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) - [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017) - [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018) - [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020) - [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020) - [Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue](https://mlsanity.com/paper/1803.11293/) (2018) - [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) - [Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning](https://mlsanity.com/paper/2112.05240/) (2021) - [Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network](https://mlsanity.com/paper/2201.11333/) (2022) - [Deep learning-based virtual refocusing of images using an engineered point-spread function](https://mlsanity.com/paper/2012.11892/) (2020) ## Built on by (most cited first, 13 of 13) - [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022) - [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) - [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) - [Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization](https://mlsanity.com/paper/2407.04245/) (2024) - [AV-GAN: Attention-Based Varifocal Generative Adversarial Network for Uneven Medical Image Translation](https://mlsanity.com/paper/2404.10714/) (2024) - [Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images](https://mlsanity.com/paper/2407.00098/) (2024) - [Single color digital H&E staining with In-and-Out Net](https://mlsanity.com/paper/2405.13278/) (2024) - [Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining](https://mlsanity.com/paper/2409.05255/) (2024) - [IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions](https://mlsanity.com/paper/2505.09831/) (2025) - [From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging](https://mlsanity.com/paper/2505.11394/) (2025) - [PRINTER:Deformation-Aware Adversarial Learning for Virtual IHC Staining with In Situ Fidelity](https://mlsanity.com/paper/2509.01214/) (2025) - [Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality](https://mlsanity.com/paper/2511.04615/) (2025) ## Abstract (arXiv) Pathological diagnosis relies on the visual inspection of histologically stained thin tissue specimens, where different types of stains are applied to bring contrast to and highlight various desired histological features. However, the destructive histochemical staining procedures are usually irreversible, making it very difficult to obtain multiple stains on the same tissue section. Here, we demonstrate a virtual stain transfer framework via a cascaded deep neural network (C-DNN) to digitally transform hematoxylin and eosin (H&E) stained tissue images into other types of histological stains. Unlike a single neural network structure which only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E and then performs stain transfer from H&E to the domain of the other stain in a cascaded manner. This cascaded structure in the training phase allows the model to directly exploit histochemically stained image data on both H&E and the target special stain of interest. This advantage alleviates the challenge of paired data acquisition and improves the image quality and color accuracy of the virtual stain transfer from H&E to another stain. We validated the superior performance of this C-DNN approach using kidney needle core biopsy tissue sections and successfully transferred the H&E-stained tissue images into virtual PAS (periodic acid-Schiff) stain. This method provides high-quality virtual images of special stains using existing, histochemically stained slides and creates new opportunities in digital pathology by performing highly accurate stain-to-stain transformations.