# Neural Stain-Style Transfer Learning using GAN for Histopathological Images

arXiv: https://arxiv.org/abs/1710.08543 · HTML: https://mlsanity.com/paper/1710.08543/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2017.

## Built on by (most cited first, 18 of 18)

- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix](https://mlsanity.com/paper/2204.11425/) (2022)
- [Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning](https://mlsanity.com/paper/2112.08837/) (2021)
- [Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review](https://mlsanity.com/paper/2305.03546/) (2023)
- [StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images](https://mlsanity.com/paper/2403.09302/) (2024)
- [Deep Learning-based Frozen Section to FFPE Translation](https://mlsanity.com/paper/2107.11786/) (2021)
- [H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression](https://mlsanity.com/paper/2201.06329/) (2022)
- [Augment like there's no tomorrow: Consistently performing neural networks for medical imaging](https://mlsanity.com/paper/2206.15274/) (2022)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [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)
- [Deep learning based domain adaptation for mitochondria segmentation on EM volumes](https://mlsanity.com/paper/2202.10773/) (2022)
- [MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification](https://mlsanity.com/paper/2509.00311/) (2025)
- [Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology](https://mlsanity.com/paper/2411.09373/) (2024)
- [Multi-Channel Auto-Encoders and a Novel Dataset for Learning Domain Invariant Representations of Histopathology Images](https://mlsanity.com/paper/2107.07271/) (2021)
- [ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)](https://mlsanity.com/paper/2302.03140/) (2023)
- [ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization](https://mlsanity.com/paper/2305.06511/) (2023)
- [Staining normalization in histopathology: Method benchmarking using multicenter dataset](https://mlsanity.com/paper/2506.19106/) (2025)

## Abstract (arXiv)

Performance of data-driven network for tumor classification varies with stain-style of histopathological images. This article proposes the stain-style transfer (SST) model based on conditional generative adversarial networks (GANs) which is to learn not only the certain color distribution but also the corresponding histopathological pattern. Our model considers feature-preserving loss in addition to well-known GAN loss. Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images. The model is examined using the CAMELYON16 dataset.