COVID detection and severity prediction with 3D-ConvNeXt and custom pretrainings

  • 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.

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Author:Daniel KienzleGND, Julian LorenzGND, Robin SchönGND, Katja LudwigGND, Rainer LienhartGND
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Parent Title (English):Computer Vision – ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII
Place of publication:Berlin
Editor:Leonid Karlinsky, Tomer Michaeli, Ko Nishino
Type:Conference Proceeding
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2022/08/18
First Page:500
Last Page:516
Series:Lecture Notes in Computer Science ; 13807
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht