# Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge

arXiv: https://arxiv.org/abs/2109.00965 · HTML: https://mlsanity.com/paper/2109.00965/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 1 Sep 2021.

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

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation](https://mlsanity.com/paper/1909.11524/) (2019)

## Built on by (most cited first, 3 of 3)

- [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)
- [Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer](https://mlsanity.com/paper/2209.09193/) (2022)

## Abstract (arXiv)

We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generalization (MIDOG) Challenge at MICCAI 2021.