{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/custom-pretrainings-and-adapted-3d-convnext","title":"COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings","arxiv_id":"2206.15073","date":"2022-06-30","proceeding":null,"authors":["Daniel Kienzle","Julian Lorenz","Robin Schön","Katja Ludwig","Rainer Lienhart"],"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.","url_abs":"https://arxiv.org/abs/2206.15073v2","url_pdf":"https://arxiv.org/pdf/2206.15073v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"custom-pretrainings-and-adapted-3d-convnext","repo_url":"https://github.com/kiedani/submission_2nd_covid19_competition","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"severity-prediction","task_name":"severity prediction"}],"methods":[{"method_slug":"convnext","method_name":"ConvNeXt"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}