Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866909170839584768 |
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| author | Lee, Chia-Yen Chien, Hsiang-Chin Wang, Ching-Ping Yen, Hong Zhen, Kai-Wen Lin, Hong-Kun |
| author_facet | Lee, Chia-Yen Chien, Hsiang-Chin Wang, Ching-Ping Yen, Hong Zhen, Kai-Wen Lin, Hong-Kun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_13588 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge Lee, Chia-Yen Chien, Hsiang-Chin Wang, Ching-Ping Yen, Hong Zhen, Kai-Wen Lin, Hong-Kun Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2202.13588 |