# Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency

arXiv: https://arxiv.org/abs/2410.01072 · HTML: https://mlsanity.com/paper/2410.01072/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [StainGAN: Stain Style Transfer for Digital Histological Images](https://mlsanity.com/paper/1804.01601/) (2018)
- [HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms](https://mlsanity.com/paper/2011.11731/) (2020)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis](https://mlsanity.com/paper/1901.04059/) (2019)
- [Virtual staining for mitosis detection in Breast Histopathology](https://mlsanity.com/paper/2003.07801/) (2020)

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

- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology](https://mlsanity.com/paper/2503.20880/) (2025)
- [CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction](https://mlsanity.com/paper/2503.02064/) (2025)
- [Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality](https://mlsanity.com/paper/2511.04615/) (2025)
- [SheafStain: Sheaf-Theoretic Schr"odinger Bridge for Spatially and Biologically Coherent Virtual Staining](https://mlsanity.com/paper/2606.11846/) (2026)
- [Seamless Whole Slide Label-Free Virtual Staining](https://mlsanity.com/paper/2609.10914/) (2026)

## Abstract (arXiv)

Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases. Virtual staining from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) allows the automatic production of other useful IHC stains without the expensive physical staining process. However, current virtual WSI generation methods based on tile-wise processing often suffer from inconsistencies in content, texture, and color at tile boundaries. These inconsistencies lead to artifacts that compromise image quality and potentially hinder accurate clinical assessment and diagnoses. To address this limitation, we propose a novel consistent WSI synthesis network, CC-WSI-Net, that extends GAN models to produce seamless synthetic whole slide images. Our CC-WSI-Net integrates a content- and color-consistency supervisor, ensuring consistency across tiles and facilitating the generation of seamless synthetic WSIs while ensuring Sox10 immunohistochemistry accuracy in melanocyte detection. We validate our method through extensive image-quality analyses, objective detection assessments, and a subjective survey with pathologists. By generating high-quality synthetic WSIs, our method opens doors for advanced virtual staining techniques with broader applications in research and clinical care.