# Stain Style Transfer using Transitive Adversarial Networks

arXiv: https://arxiv.org/abs/1910.10330 · HTML: https://mlsanity.com/paper/1910.10330/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review](https://mlsanity.com/paper/2305.03546/) (2023)
- [Generative Adversarial Networks for Stain Normalisation in Histopathology](https://mlsanity.com/paper/2308.02851/) (2023)
- [Multi-Channel Auto-Encoders and a Novel Dataset for Learning Domain Invariant Representations of Histopathology Images](https://mlsanity.com/paper/2107.07271/) (2021)
- [Biomedical Image Reconstruction: A Survey](https://mlsanity.com/paper/2301.11813/) (2023)
- [ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization](https://mlsanity.com/paper/2305.06511/) (2023)
- [Color Deconvolution applied to Domain Adaptation in HER2 histopathological images](https://mlsanity.com/paper/2305.07404/) (2023)
- [StainPresetNet: Stain Preset Network for Fast Multi-to-Multi Stain Normalization](https://mlsanity.com/paper/2609.01146/) (2026)

## Abstract (arXiv)

Digitized pathological diagnosis has been in increasing demand recently. It is well known that color information is critical to the automatic and visual analysis of pathological slides. However, the color variations due to various factors not only have negative impact on pathologist's diagnosis, but also will reduce the robustness of the algorithms. The factors that cause the color differences are not only in the process of making the slices, but also in the process of digitization. Different strategies have been proposed to alleviate the color variations. Most of such techniques rely on collecting color statistics to perform color matching across images and highly dependent on a reference template slide. Since the pathological slides between hospitals are usually unpaired, these methods do not yield good matching results. In this work, we propose a novel network that we refer to as Transitive Adversarial Networks (TAN) to transfer the color information among slides from different hospitals or centers. It is not necessary for an expert to pick a representative reference slide in the proposed TAN method. We compare the proposed method with the state-of-the-art methods quantitatively and qualitatively. Compared with the state-of-the-art methods, our method yields an improvement of 0.87dB in terms of PSNR, demonstrating the effectiveness of the proposed TAN method in stain style transfer.