Abstract
With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis model based on PVTv2 and the multiple voting mechanism. To accommodate the different dimensions of the image input, we classified the images using the Transformer model, sampled the images in the dataset according to the normal distribution, and fed the sampling results into the PVTv2 model for training. A large number of experiments on the COV19-CT-DB dataset demonstrate the effectiveness of the proposed method. Our method won the sixth place in the (2nd) COVID19 Detection Challenge of ECCV 2022 Workshop: AI-enabled Medical Image Analysis - Digital Pathology & Radiology/COVID19. Our code is publicly available at https://github.com/MenSan233/Team-Dslab-Solution.
L. Zheng and J. Fang—Equal contribution.
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Acknowledgements
This work was partially supported by the National Key R &D Program of China under Grant No. 2020YFC0832500, National Natural Science Foundation of China under Grant No. 61402210, Ministry of Education-China Mobile Research Foundation under Grant No. MCM20170206, Fundamental Research Funds for the Central Universities under Grant No. lzujbky-2021-sp43, lzujbky-2019-kb51 and lzujbky-2018-k12, Science and Technology Plan of Qinghai Province under Grant No.2020-GX-164. We also acknowledge Mr. Rui Zhao for his contribution to this paper.
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Zheng, L. et al. (2023). PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13807. Springer, Cham. https://doi.org/10.1007/978-3-031-25082-8_35
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