# MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss

arXiv: https://arxiv.org/abs/2204.09782 · HTML: https://mlsanity.com/paper/2204.09782/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 48 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [DRIT++: Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1905.01270/) (2019)
- [Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology](https://mlsanity.com/paper/1902.06543/) (2019)
- [StainGAN: Stain Style Transfer for Digital Histological Images](https://mlsanity.com/paper/1804.01601/) (2018)
- [A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment](https://mlsanity.com/paper/1607.06140/) (2016)
- [Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations](https://mlsanity.com/paper/2107.12357/) (2021)
- [StainNet: a fast and robust stain normalization network](https://mlsanity.com/paper/2012.12535/) (2020)
- [VIDIT: Virtual Image Dataset for Illumination Transfer](https://mlsanity.com/paper/2005.05460/) (2020)
- [Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance](https://mlsanity.com/paper/2008.02101/) (2020)
- [Towards Histopathological Stain Invariance by Unsupervised Domain Augmentation using Generative Adversarial Networks](https://mlsanity.com/paper/2012.12413/) (2020)
- [Unsupervised Image Translation using Adversarial Networks for Improved Plant Disease Recognition](https://mlsanity.com/paper/1909.11915/) (2019)
- [Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles](https://mlsanity.com/paper/1902.03582/) (2019)
- [Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer](https://mlsanity.com/paper/1906.00617/) (2019)
- [Magnifying Networks for Images with Billions of Pixels](https://mlsanity.com/paper/2112.06121/) (2021)

## Built on by (most cited first, 4 of 4)

- [EXAONEPath 1.0 Patch-level Foundation Model for Pathology](https://mlsanity.com/paper/2408.00380/) (2024)
- [HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks](https://mlsanity.com/paper/2210.06909/) (2022)
- [Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation](https://mlsanity.com/paper/2311.06552/) (2023)
- [StainPresetNet: Stain Preset Network for Fast Multi-to-Multi Stain Normalization](https://mlsanity.com/paper/2609.01146/) (2026)

## Abstract (arXiv)

Histopathology relies on the analysis of microscopic tissue images to diagnose disease. A crucial part of tissue preparation is staining whereby a dye is used to make the salient tissue components more distinguishable. However, differences in laboratory protocols and scanning devices result in significant confounding appearance variation in the corresponding images. This variation increases both human error and the inter-rater variability, as well as hinders the performance of automatic or semi-automatic methods. In the present paper we introduce an unsupervised adversarial network to translate (and hence normalize) whole slide images across multiple data acquisition domains. Our key contributions are: (i) an adversarial architecture which learns across multiple domains with a single generator-discriminator network using an information flow branch which optimizes for perceptual loss, and (ii) the inclusion of an additional feature extraction network during training which guides the transformation network to keep all the structural features in the tissue image intact. We: (i) demonstrate the effectiveness of the proposed method firstly on H&E slides of 120 cases of kidney cancer, as well as (ii) show the benefits of the approach on more general problems, such as flexible illumination based natural image enhancement and light source adaptation.