mlsanityData to 14 Oct 2026

COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings

arXiv 2206.15073 ↗First posted 30 Jun 2022eess.IV cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 20 of its 49 references that are in the map. First posted on arXiv 30 Jun 2022.

Builds on

20 of 49 references.

References · 20

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8 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 0; 2026: 1.

6 papers.

  1. 2024PIPNet3D: Interpretable Detection of Alzheimer in MRI Scanscited by 3
  2. 2024Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classificationcited by 2
  3. 2023Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentationcited by 1
  4. 20232D and 3D CNN-Based Fusion Approach for COVID-19 Severity Prediction from 3D CT-Scanscited by 0
  5. 2023The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private datacited by 0
  6. 2026A Multi-Source Ultrasound Benchmark Revealing the Limits of Contemporary Self-Supervised Anomaly Detection Methodscited by 0