COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings
Abstract
Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severity of lung damage and the detection of a COVID-infection using three-dimensional CT-data. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we design and analyze different pretraining methods specifically designed to improve the models ability to handle three-dimensional CT-data. We rank 2nd in the 1st COVID19 Severity Detection Challenge and 3rd in the 2nd COVID19 Detection Challenge.
Cite
Text
Kienzle et al. "COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25082-8_33Markdown
[Kienzle et al. "COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/kienzle2022eccvw-covid/) doi:10.1007/978-3-031-25082-8_33BibTeX
@inproceedings{kienzle2022eccvw-covid,
title = {{COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings}},
author = {Kienzle, Daniel and Lorenz, Julian and Schön, Robin and Ludwig, Katja and Lienhart, Rainer},
booktitle = {European Conference on Computer Vision Workshops},
year = {2022},
pages = {500-516},
doi = {10.1007/978-3-031-25082-8_33},
url = {https://mlanthology.org/eccvw/2022/kienzle2022eccvw-covid/}
}