论文标题
在Conic Challenge中使用多尺度的SwintrantransFormer-HTC与数据增强
Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge
论文作者
论文摘要
大肠癌是全球最常见的癌症之一，因此早期的病理检查非常重要。但是，识别临床中H＆E图像上细胞的数量和类型是耗时和劳动力密集型的。因此，Conic Challenge 2022提出了自动分割和分类任务，并计算病理部分中H＆E图像的细胞组成。我们提出了一种具有HTC的多尺度Swin Transferaler，用于此挑战，并应用了已知的归一化方法来生成更多的增强数据。最后，我们的策略表明，多尺度在识别不同规模的特征并引起了模型的识别，发挥了至关重要的作用。
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.
