# Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis

arXiv: https://arxiv.org/abs/2002.00647 · HTML: https://mlsanity.com/paper/2002.00647/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 23 of 23)

- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization](https://mlsanity.com/paper/2206.12694/) (2022)
- [Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning](https://mlsanity.com/paper/2112.08837/) (2021)
- [RestainNet: a self-supervised digital re-stainer for stain normalization](https://mlsanity.com/paper/2202.13804/) (2022)
- [ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology](https://mlsanity.com/paper/2306.04527/) (2023)
- [Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning](https://mlsanity.com/paper/2310.16954/) (2023)
- [StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images](https://mlsanity.com/paper/2403.09302/) (2024)
- [Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images](https://mlsanity.com/paper/2301.09431/) (2023)
- [Dataset Distillation for Histopathology Image Classification](https://mlsanity.com/paper/2408.09709/) (2024)
- [HistoStarGAN: A Unified Approach to Stain Normalisation, Stain Transfer and Stain Invariant Segmentation in Renal Histopathology](https://mlsanity.com/paper/2210.09798/) (2022)
- [Generative Adversarial Networks for Stain Normalisation in Histopathology](https://mlsanity.com/paper/2308.02851/) (2023)
- [Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks](https://mlsanity.com/paper/2106.07020/) (2021)
- [A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels](https://mlsanity.com/paper/2205.10373/) (2022)
- [Cross-channel Perception Learning for H&E-to-IHC Virtual Staining](https://mlsanity.com/paper/2506.07559/) (2025)
- [Inter-Semantic Domain Adversarial in Histopathological Images](https://mlsanity.com/paper/2201.09041/) (2022)
- [Comparative analysis of segmentation and generative models for fingerprint retrieval task](https://mlsanity.com/paper/2209.06172/) (2022)
- [ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization](https://mlsanity.com/paper/2305.06511/) (2023)
- [Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification](https://mlsanity.com/paper/2310.09981/) (2023)
- [Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization](https://mlsanity.com/paper/2409.20340/) (2024)
- [LSA: Latent Style Augmentation Towards Stain-Agnostic Cervical Cancer Screening](https://mlsanity.com/paper/2503.06563/) (2025)
- [StainPIDR: A Pathological Image Decouplingand Reconstruction Method for Stain Normalization Based on Color Vector Quantization and Structure Restaining](https://mlsanity.com/paper/2506.17879/) (2025)
- [Staining normalization in histopathology: Method benchmarking using multicenter dataset](https://mlsanity.com/paper/2506.19106/) (2025)
- [One Model to Magnify Them All: Efficient Scale-Invariant Histopathology via Conditional Normalization and Continuous Magnification Training](https://mlsanity.com/paper/2608.09403/) (2026)

## Abstract (arXiv)

The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to Interpretive disparity among pathologists more is one of the main challenges in designing robust and flexible systems for automated analysis. To address the staining color variations, several methods for normalizing stain have been proposed. In our proposed method, a Stain-to-Stain Translation (STST) approach is used to stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images, which learns not only the specific color distribution but also the preserves corresponding histopathological pattern. We perform the process of translation based on the pix2pix framework, which uses the conditional generator adversarial networks (cGANs). Our approach showed excellent results, both mathematically and experimentally against the state of the art methods. We have made the source code publicly available.