# Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector

arXiv: https://arxiv.org/abs/2203.01321 · HTML: https://mlsanity.com/paper/2203.01321/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Probabilistic two-stage detection](https://mlsanity.com/paper/2103.07461/) (2021)
- [CoNIC: Colon Nuclei Identification and Counting Challenge 2022](https://mlsanity.com/paper/2111.14485/) (2021)

## Built on by (most cited first, 1 of 1)

- [A review of machine learning approaches, challenges and prospects for computational tumor pathology](https://mlsanity.com/paper/2206.01728/) (2022)

## Abstract (arXiv)

Cancer is one of the leading causes of death in the developed world. Cancer diagnosis is performed through the microscopic analysis of a sample of suspicious tissue. This process is time consuming and error prone, but Deep Learning models could be helpful for pathologists during cancer diagnosis. We propose to change the CenterNet2 object detection model to also perform instance segmentation, which we call SegCenterNet2. We train SegCenterNet2 in the CoNIC challenge dataset and show that it performs better than Mask R-CNN in the competition metrics.