# Strategies for Training Stain Invariant CNNs

arXiv: https://arxiv.org/abs/1810.10338 · HTML: https://mlsanity.com/paper/1810.10338/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [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)
- [Unsupervised Latent Stain Adaptation for Computational Pathology](https://mlsanity.com/paper/2406.19081/) (2024)
- [Resource Efficient Multi-stain Kidney Glomeruli Segmentation via Self-supervision](https://mlsanity.com/paper/2412.15389/) (2024)
- [VLM-based Prompts as the Optimal Assistant for Unpaired Histopathology Virtual Staining](https://mlsanity.com/paper/2504.15545/) (2025)

## Abstract (arXiv)

An important part of Digital Pathology is the analysis of multiple digitised whole slide images from differently stained tissue sections. It is common practice to mount consecutive sections containing corresponding microscopic structures on glass slides, and to stain them differently to highlight specific tissue components. These multiple staining modalities result in very different images but include a significant amount of consistent image information. Deep learning approaches have recently been proposed to analyse these images in order to automatically identify objects of interest for pathologists. These supervised approaches require a vast amount of annotations, which are difficult and expensive to acquire---a problem that is multiplied with multiple stainings. This article presents several training strategies that make progress towards stain invariant networks. By training the network on one commonly used staining modality and applying it to images that include corresponding but differently stained tissue structures, the presented unsupervised strategies demonstrate significant improvements over standard training strategies.