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

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Chia-Yen, Lee, Hsiang-Chin Chien, Ching-Ping, Wang, Yen, Hong, Kai-Wen, Zhen, Hong-Kun, Lin
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.04.2024
Subjects
Cancer
Computer Science - Computer Vision and Pattern Recognition
Data augmentation
Image classification
Image segmentation
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2202.13588