# Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations

arXiv: https://arxiv.org/abs/2107.12357 · HTML: https://mlsanity.com/paper/2107.12357/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [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)
- [Distribution Matching Losses Can Hallucinate Features in Medical Image Translation](https://mlsanity.com/paper/1805.08841/) (2018)
- [StainGAN: Stain Style Transfer for Digital Histological Images](https://mlsanity.com/paper/1804.01601/) (2018)
- [Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential](https://mlsanity.com/paper/2004.14936/) (2020)
- [Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation](https://mlsanity.com/paper/2006.14566/) (2020)
- [Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning](https://mlsanity.com/paper/2007.12578/) (2020)

## Built on by (most cited first, 30 of 31)

- [RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization](https://mlsanity.com/paper/2206.12694/) (2022)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation](https://mlsanity.com/paper/2206.01737/) (2022)
- [Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology](https://mlsanity.com/paper/2206.09769/) (2022)
- [A comparative evaluation of image-to-image translation methods for stain transfer in histopathology](https://mlsanity.com/paper/2303.17009/) (2023)
- [ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology](https://mlsanity.com/paper/2306.04527/) (2023)
- [EXAONEPath 1.0 Patch-level Foundation Model for Pathology](https://mlsanity.com/paper/2408.00380/) (2024)
- [Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data](https://mlsanity.com/paper/2208.13944/) (2022)
- [MouseGAN++: Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain](https://mlsanity.com/paper/2212.01825/) (2022)
- [Denoising Diffusion Medical Models](https://mlsanity.com/paper/2304.09383/) (2023)
- [MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss](https://mlsanity.com/paper/2204.09782/) (2022)
- [Stain-invariant self supervised learning for histopathology image analysis](https://mlsanity.com/paper/2211.07590/) (2022)
- [Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images](https://mlsanity.com/paper/2301.09431/) (2023)
- [Uncertainty-guided annotation enhances segmentation with the human-in-the-loop](https://mlsanity.com/paper/2404.07208/) (2024)
- [HistoStarGAN: A Unified Approach to Stain Normalisation, Stain Transfer and Stain Invariant Segmentation in Renal Histopathology](https://mlsanity.com/paper/2210.09798/) (2022)
- [Unsupervised Latent Stain Adaptation for Computational Pathology](https://mlsanity.com/paper/2406.19081/) (2024)
- [A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancement](https://mlsanity.com/paper/2304.01864/) (2023)
- [A Laplacian Pyramid Based Generative H&E Stain Augmentation Network](https://mlsanity.com/paper/2305.14301/) (2023)
- [ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation](https://mlsanity.com/paper/2403.06545/) (2024)
- [Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from Duplex to Monoplex IHC Images](https://mlsanity.com/paper/2403.07389/) (2024)
- [Mask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model](https://mlsanity.com/paper/2407.11664/) (2024)
- [Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images](https://mlsanity.com/paper/2307.11808/) (2023)
- [StyleX: A Trainable Metric for X-ray Style Distances](https://mlsanity.com/paper/2405.14718/) (2024)
- [Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration](https://mlsanity.com/paper/2407.13632/) (2024)
- [Multi-Modality Microscopy Image Style Transfer for Nuclei Segmentation](https://mlsanity.com/paper/2111.12138/) (2021)
- [H&E Stain Normalization using U-Net](https://mlsanity.com/paper/2211.05420/) (2022)
- [Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation](https://mlsanity.com/paper/2311.06552/) (2023)
- [LSA: Latent Style Augmentation Towards Stain-Agnostic Cervical Cancer Screening](https://mlsanity.com/paper/2503.06563/) (2025)
- [Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models](https://mlsanity.com/paper/2507.02148/) (2025)
- [STAMP: Multi-pattern Attention-aware Multiple Instance Learning for STAS Diagnosis in Multi-center Histopathology Images](https://mlsanity.com/paper/2508.10473/) (2025)

## Abstract (arXiv)

In digital pathology, different staining procedures and scanners cause substantial color variations in whole-slide images (WSIs), especially across different laboratories. These color shifts result in a poor generalization of deep learning-based methods from the training domain to external pathology data. To increase test performance, stain normalization techniques are used to reduce the variance between training and test domain. Alternatively, color augmentation can be applied during training leading to a more robust model without the extra step of color normalization at test time. We propose a novel color augmentation technique, HistAuGAN, that can simulate a wide variety of realistic histology stain colors, thus making neural networks stain-invariant when applied during training. Based on a generative adversarial network (GAN) for image-to-image translation, our model disentangles the content of the image, i.e., the morphological tissue structure, from the stain color attributes. It can be trained on multiple domains and, therefore, learns to cover different stain colors as well as other domain-specific variations introduced in the slide preparation and imaging process. We demonstrate that HistAuGAN outperforms conventional color augmentation techniques on a classification task on the publicly available dataset Camelyon17 and show that it is able to mitigate present batch effects.