# Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge

arXiv: https://arxiv.org/abs/2202.13588 · HTML: https://mlsanity.com/paper/2202.13588/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 28 Feb 2022.

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

- [Hybrid Task Cascade for Instance Segmentation](https://mlsanity.com/paper/1901.07518/) (2019)
- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [CoNIC: Colon Nuclei Identification and Counting Challenge 2022](https://mlsanity.com/paper/2111.14485/) (2021)
- [Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections](https://mlsanity.com/paper/1802.04427/) (2018)

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

- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)

## Abstract (arXiv)

Colorectal cancer is one of the most common cancers worldwide, so early pathological examination is very important. However, it is time-consuming and labor-intensive to identify the number and type of cells on H&E images in clinical. Therefore, automatic segmentation and classification task and counting the cellular composition of H&E images from pathological sections is proposed by CoNIC Challenge 2022. We proposed a multi-scale Swin transformer with HTC for this challenge, and also applied the known normalization methods to generate more augmentation data. Finally, our strategy showed that the multi-scale played a crucial role to identify different scale features and the augmentation arose the recognition of model.