Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge
arxiv(2022)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要