# Editable Stain Transformation Of Histological Images Using Unpaired GANs

arXiv: https://arxiv.org/abs/2312.03647 · HTML: https://mlsanity.com/paper/2312.03647/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Closed-Form Factorization of Latent Semantics in GANs](https://mlsanity.com/paper/2007.06600/) (2020)
- [Editing in Style: Uncovering the Local Semantics of GANs](https://mlsanity.com/paper/2004.14367/) (2020)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [Sequential Attention GAN for Interactive Image Editing](https://mlsanity.com/paper/1812.08352/) (2018)
- [GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis](https://mlsanity.com/paper/1901.04059/) (2019)
- [xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems](https://mlsanity.com/paper/2002.10438/) (2020)
- [Optimizing Latent Space Directions For GAN-based Local Image Editing](https://mlsanity.com/paper/2111.12583/) (2021)
- [xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network](https://mlsanity.com/paper/2306.15760/) (2023)

## Built on by (most cited first, 1 of 1)

- [Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization](https://mlsanity.com/paper/2409.20340/) (2024)

## Abstract (arXiv)

Double staining in histopathology, particularly for metaplastic breast cancer, typically employs H&E and P63 dyes. However, P63's tissue damage and high cost necessitate alternative methods. This study introduces xAI-CycleGAN, an advanced architecture combining Mask CycleGAN with explainability features and structure-preserving capabilities for transforming H&E stained breast tissue images into P63-like images. The architecture allows for output editing, enhancing resemblance to actual images and enabling further model refinement. We showcase xAI-CycleGAN's efficacy in maintaining structural integrity and generating high-quality images. Additionally, a histopathologist survey indicates the generated images' realism is often comparable to actual images, validating our model's high-quality output.