Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 28 Feb 2022.
Builds on
4 of 11 references.
References · 4
- 2019Hybrid Task Cascade for Instance Segmentationcited by 479
- 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
- 2021CoNIC: Colon Nuclei Identification and Counting Challenge 2022cited by 44
- 2018Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sectionscited by 2
Built on by
Citing papers per year: 2022: 1.
1 paper.