# DSA-CycleGAN: A Domain Shift Aware CycleGAN for Robust Multi-Stain Glomeruli Segmentation

arXiv: https://arxiv.org/abs/2604.18368 · HTML: https://mlsanity.com/paper/2604.18368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests](https://mlsanity.com/paper/1509.02237/) (2015)
- [CycleGAN, a Master of Steganography](https://mlsanity.com/paper/1712.02950/) (2017)
- [Breaking the cycle -- Colleagues are all you need](https://mlsanity.com/paper/1911.10538/) (2019)
- [Adversarial Self-Defense for Cycle-Consistent GANs](https://mlsanity.com/paper/1908.01517/) (2019)
- [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024)
- [Towards Histopathological Stain Invariance by Unsupervised Domain Augmentation using Generative Adversarial Networks](https://mlsanity.com/paper/2012.12413/) (2020)
- [SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2103.16219/) (2021)
- [A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation](https://mlsanity.com/paper/2107.11945/) (2021)
- [Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning](https://mlsanity.com/paper/2112.08837/) (2021)
- [Self adversarial attack as an augmentation method for immunohistochemical stainings](https://mlsanity.com/paper/2103.11362/) (2021)
- [Towards Measuring Domain Shift in Histopathological Stain Translation in an Unsupervised Manner](https://mlsanity.com/paper/2205.04368/) (2022)
- [HistoStarGAN: A Unified Approach to Stain Normalisation, Stain Transfer and Stain Invariant Segmentation in Renal Histopathology](https://mlsanity.com/paper/2210.09798/) (2022)
- [CycleGAN with Better Cycles](https://mlsanity.com/paper/2408.15374/) (2024)

## Abstract (arXiv)

A key challenge in segmentation in digital histopathology is inter- and intra-stain variations as it reduces model performance. Labelling each stain is expensive and time-consuming so methods using stain transfer via CycleGAN, have been developed for training multi-stain segmentation models using labels from a single stain. Nevertheless, CycleGAN tends to introduce noise during translation because of the one-to-many nature of some stain pairs, which conflicts with its cycle consistency loss. To address this, we propose the Domain Shift Aware CycleGAN, which reduces the presence of such noise. Furthermore, we evaluate several advances from the field of machine learning aimed at resolving similar problems and compare their effectiveness against DSA-CycleGAN in the context of multi-stain glomeruli segmentation. Experiments demonstrate that DSA-CycleGAN not only improves segmentation performance in glomeruli segmentation but also outperforms other methods in reducing noise. This is particularly evident when translating between biologically distinct stains. The code is publicly available at https://github.com/zeeshannisar/DSA-CycleGAN.