PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer
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 .
Cite
Text
Zheng et al. "PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25082-8_35Markdown
[Zheng et al. "PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/zheng2022eccvw-pvtcov19d/) doi:10.1007/978-3-031-25082-8_35BibTeX
@inproceedings{zheng2022eccvw-pvtcov19d,
title = {{PVT-COV19D: COVID-19 Detection Through Medical Image Classification Based on Pyramid Vision Transformer}},
author = {Zheng, Lilang and Fang, Jiaxuan and Tang, Xiaorun and Li, Hanzhang and Fan, Jiaxin and Wang, Tianyi and Zhou, Rui and Yan, Zhaoyan},
booktitle = {European Conference on Computer Vision Workshops},
year = {2022},
pages = {526-536},
doi = {10.1007/978-3-031-25082-8_35},
url = {https://mlanthology.org/eccvw/2022/zheng2022eccvw-pvtcov19d/}
}