# Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning

arXiv: https://arxiv.org/abs/2007.12578 · HTML: https://mlsanity.com/paper/2007.12578/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

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

- [Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations](https://mlsanity.com/paper/2107.12357/) (2021)
- [RestainNet: a self-supervised digital re-stainer for stain normalization](https://mlsanity.com/paper/2202.13804/) (2022)
- [Generative Adversarial Networks for Stain Normalisation in Histopathology](https://mlsanity.com/paper/2308.02851/) (2023)
- [Style-Extracting Diffusion Models for Semi-Supervised Histopathology Segmentation](https://mlsanity.com/paper/2403.14429/) (2024)
- [A Laplacian Pyramid Based Generative H&E Stain Augmentation Network](https://mlsanity.com/paper/2305.14301/) (2023)
- [Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction](https://mlsanity.com/paper/2311.10305/) (2023)

## Abstract (arXiv)

Computational histopathology image diagnosis becomes increasingly popular and important, where images are segmented or classified for disease diagnosis by computers. While pathologists do not struggle with color variations in slides, computational solutions usually suffer from this critical issue. To address the issue of color variations in histopathology images, this study proposes two stain style transfer models, SSIM-GAN and DSCSI-GAN, based on the generative adversarial networks. By cooperating structural preservation metrics and feedback of an auxiliary diagnosis net in learning, medical-relevant information presented by image texture, structure, and chroma-contrast features is preserved in color-normalized images. Particularly, the smart treat of chromatic image content in our DSCSI-GAN model helps to achieve noticeable normalization improvement in image regions where stains mix due to histological substances co-localization. Extensive experimentation on public histopathology image sets indicates that our methods outperform prior arts in terms of generating more stain-consistent images, better preserving histological information in images, and obtaining significantly higher learning efficiency. Our python implementation is published on https://github.com/hanwen0529/DSCSI-GAN.