# Virtual staining for mitosis detection in Breast Histopathology

arXiv: https://arxiv.org/abs/2003.07801 · HTML: https://mlsanity.com/paper/2003.07801/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Digital staining in optical microscopy using deep learning -- a review](https://mlsanity.com/paper/2303.08140/) (2023)
- [Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency](https://mlsanity.com/paper/2410.01072/) (2024)
- [Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images](https://mlsanity.com/paper/2109.00869/) (2021)
- [Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields](https://mlsanity.com/paper/2403.17808/) (2024)
- [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)
- [Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology](https://mlsanity.com/paper/2301.13128/) (2023)
- [Scalable, Trustworthy Generative Model for Virtual Multi-Staining from H&E Whole Slide Images](https://mlsanity.com/paper/2407.00098/) (2024)
- [Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data](https://mlsanity.com/paper/2504.09899/) (2025)
- [On the Value of PHH3 for Mitotic Figure Detection on H&E-stained Images](https://mlsanity.com/paper/2406.19899/) (2024)
- [Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining](https://mlsanity.com/paper/2601.02806/) (2026)

## Abstract (arXiv)

We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potential of this application to scale the number of training samples without the need for manual annotations.