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

28 Feb 2022 · Chia-Yen Lee, Hsiang-Chin Chien, Ching-Ping Wang, Hong Yen, Kai-Wen Zhen, Hong-Kun Lin ·

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.

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Data Augmentation

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1x1 Convolution • Convolution • Dense Connections • FPN • HTC • Layer Normalization • Linear Layer • Multi-Head Attention • Residual Connection • RoIAlign • RPN • Scaled Dot-Product Attention • Softmax • Stochastic Depth • Swin Transformer

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

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