# Stain Isolation-based Guidance for Improved Stain Translation

arXiv: https://arxiv.org/abs/2207.00431 · HTML: https://mlsanity.com/paper/2207.00431/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance](https://mlsanity.com/paper/2008.02101/) (2020)
- [Domain Adaptation-based Augmentation for Weakly Supervised Nuclei Detection](https://mlsanity.com/paper/1907.04681/) (2019)

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

- [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)

## Abstract (arXiv)

Unsupervised and unpaired domain translation using generative adversarial neural networks, and more precisely CycleGAN, is state of the art for the stain translation of histopathology images. It often, however, suffers from the presence of cycle-consistent but non structure-preserving errors. We propose an alternative approach to the set of methods which, relying on segmentation consistency, enable the preservation of pathology structures. Focusing on immunohistochemistry (IHC) and multiplexed immunofluorescence (mIF), we introduce a simple yet effective guidance scheme as a loss function that leverages the consistency of stain translation with stain isolation. Qualitative and quantitative experiments show the ability of the proposed approach to improve translation between the two domains.