# Domain-Robust Mitotic Figure Detection with Style Transfer

arXiv: https://arxiv.org/abs/2109.01124 · HTML: https://mlsanity.com/paper/2109.01124/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Probabilistic two-stage detection](https://mlsanity.com/paper/2103.07461/) (2021)
- [Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge](https://mlsanity.com/paper/2108.11269/) (2021)

## Built on by (most cited first, 4 of 4)

- [Mitosis domain generalization in histopathology images -- The MIDOG challenge](https://mlsanity.com/paper/2204.03742/) (2022)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures](https://mlsanity.com/paper/2208.12587/) (2022)
- [Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer](https://mlsanity.com/paper/2209.09193/) (2022)

## Abstract (arXiv)

We propose a new training scheme for domain generalization in mitotic figure detection. Mitotic figures show different characteristics for each scanner. We consider each scanner as a 'domain' and the image distribution specified for each domain as 'style'. The goal is to train our network to be robust on scanner types by using various 'style' images. To expand the style variance, we transfer a style of the training image into arbitrary styles, by defining a module based on StarGAN. Our model with the proposed training scheme shows positive performance on MIDOG Preliminary Test-Set containing scanners never seen before.