# Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach

arXiv: https://arxiv.org/abs/1810.06415 · HTML: https://mlsanity.com/paper/1810.06415/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [A comparative evaluation of image-to-image translation methods for stain transfer in histopathology](https://mlsanity.com/paper/2303.17009/) (2023)
- [Every Pixel Has its Moments: Ultra-High-Resolution Unpaired Image-to-Image Translation via Dense Normalization](https://mlsanity.com/paper/2407.04245/) (2024)
- [Ultra-high-resolution unpaired stain transformation via Kernelized Instance Normalization](https://mlsanity.com/paper/2208.10730/) (2022)
- [Self adversarial attack as an augmentation method for immunohistochemical stainings](https://mlsanity.com/paper/2103.11362/) (2021)
- [Towards Measuring Domain Shift in Histopathological Stain Translation in an Unsupervised Manner](https://mlsanity.com/paper/2205.04368/) (2022)
- [Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data](https://mlsanity.com/paper/2504.09899/) (2025)

## Abstract (arXiv)

Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many cases, various target specific stains, including immunohistochemistry (IHC), are needed in order to highlight specific structures in the tissue. As tissue is scarce and staining procedures are tedious, it would be beneficial to generate images of stained tissue virtually. Virtual staining could also generate in-silico multiplexing of different stains on the same tissue segment. In this paper, we present a sample application that generates FAP-CK virtual IHC images from Ki67-CD8 real IHC images using an unsupervised deep learning approach based on CycleGAN. We also propose a method to deal with tiling artifacts caused by normalization layers and we validate our approach by comparing the results of tissue analysis algorithms for virtual and real images.