Advancing COVID-19 Detection in 3D CT Scans

Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui Feng, Hao Chen

Abstract: To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and remove the non-lung parts, facilitating the model to focus on lesion-related areas and reducing computational cost. We use ResNeSt-50 as the strong feature extractor, exploring various pre-trained weights and fine-tuning methods. After a thorough comparison, we initialize our model with CMC v1 pre-trained weights which incorporate COVID-19-specific prior knowledge, and perform Visual Prompt Tuning to reduce the number of training parameters. The superiority of our model is demonstrated through extensive experiments, showing significant improvements in COVID-19 detection performance compared to the baseline model. Among 12 participating teams, our method ranked 4th in the 4th COVID-19 Competition Challenge I with an average Macro F1 Score of 94.24%.