Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge
This work addresses the time-consuming task of cell identification in pathology for clinical applications, but it appears incremental as it combines existing methods like Swin transformer and HTC with known normalization techniques.
The authors tackled the problem of automatic segmentation and classification of cells in H&E images for colorectal cancer diagnosis, achieving improved performance through a multi-scale Swin transformer with HTC and data augmentation, though no concrete numbers are provided.
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.