# StainNet: a fast and robust stain normalization network

arXiv: https://arxiv.org/abs/2012.12535 · HTML: https://mlsanity.com/paper/2012.12535/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 28 of 28)

- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [Structural Cycle GAN for Virtual Immunohistochemistry Staining of Gland Markers in the Colon](https://mlsanity.com/paper/2308.13182/) (2023)
- [VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains](https://mlsanity.com/paper/2407.19113/) (2024)
- [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)
- [Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss](https://mlsanity.com/paper/2507.22092/) (2025)
- [H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression](https://mlsanity.com/paper/2201.06329/) (2022)
- [NCT-CRC-HE: Not All Histopathological Datasets Are Equally Useful](https://mlsanity.com/paper/2409.11546/) (2024)
- [Automatic dataset shift identification to support safe deployment of medical imaging AI](https://mlsanity.com/paper/2411.07940/) (2024)
- [MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss](https://mlsanity.com/paper/2204.09782/) (2022)
- [Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis](https://mlsanity.com/paper/2307.05519/) (2023)
- [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)
- [Multi-target stain normalization for histology slides](https://mlsanity.com/paper/2406.02077/) (2024)
- [BioGAN: An unpaired GAN-based image to image translation model for microbiological images](https://mlsanity.com/paper/2306.06217/) (2023)
- [Generative AI for Misalignment-Resistant Virtual Staining to Accelerate Histopathology Workflows](https://mlsanity.com/paper/2509.14119/) (2025)
- [Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization](https://mlsanity.com/paper/2310.20638/) (2023)
- [Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration](https://mlsanity.com/paper/2407.13632/) (2024)
- [H&E Stain Normalization using U-Net](https://mlsanity.com/paper/2211.05420/) (2022)
- [ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization](https://mlsanity.com/paper/2305.06511/) (2023)
- [Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation](https://mlsanity.com/paper/2311.06552/) (2023)
- [Multiple Instance Learning for Glioma Diagnosis using Hematoxylin and Eosin Whole Slide Images: An Indian Cohort Study](https://mlsanity.com/paper/2402.15832/) (2024)
- [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)
- [Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality](https://mlsanity.com/paper/2511.04615/) (2025)
- [When normalization hallucinates: unseen risks in AI-powered whole slide image processing](https://mlsanity.com/paper/2512.07426/) (2025)
- [Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification](https://mlsanity.com/paper/2601.17228/) (2026)
- [Histopathology Image Normalization via Latent Manifold Compaction](https://mlsanity.com/paper/2602.24251/) (2026)
- [StainPresetNet: Stain Preset Network for Fast Multi-to-Multi Stain Normalization](https://mlsanity.com/paper/2609.01146/) (2026)

## Abstract (arXiv)

Stain normalization often refers to transferring the color distribution of the source image to that of the target image and has been widely used in biomedical image analysis. The conventional stain normalization is regarded as constructing a pixel-by-pixel color mapping model, which only depends on one reference image, and can not accurately achieve the style transformation between image datasets. In principle, this style transformation can be well solved by the deep learning-based methods due to its complicated network structure, whereas, its complicated structure results in the low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000x100,000 whole slide image in 40 seconds.