# ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation

arXiv: https://arxiv.org/abs/2403.06545 · HTML: https://mlsanity.com/paper/2403.06545/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [StainGAN: Stain Style Transfer for Digital Histological Images](https://mlsanity.com/paper/1804.01601/) (2018)
- [Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy](https://mlsanity.com/paper/1908.03636/) (2019)
- [Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations](https://mlsanity.com/paper/2107.12357/) (2021)
- [Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection](https://mlsanity.com/paper/1907.04681/) (2019)
- [Stain Isolation-based Guidance for Improved Stain Translation](https://mlsanity.com/paper/2207.00431/) (2022)

## Built on by (most cited first, 2 of 2)

- [Mask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model](https://mlsanity.com/paper/2407.11664/) (2024)
- [MSDM: Generating Task-Specific Pathology Images with a Multimodal Conditioned Diffusion Model for Cell and Nuclei Segmentation](https://mlsanity.com/paper/2510.09121/) (2025)

## Abstract (arXiv)

The creation of in-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology. As such, it has the potential to significantly lower the cost associated with building large and pixel precise datasets needed to train supervised deep learning models. We propose a novel approach for the generation of in-silico immunohistochemistry (IHC) images by disentangling morphology specific IHC stains into separate image channels in immunofluorescence (IF) images. The proposed approach qualitatively and quantitatively outperforms baseline methods as proven by training nucleus segmentation models on the created in-silico datasets.