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Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

Deep learning has been used to assist in the analysis of medical imaging. One use is the classification of Computed Tomography (CT) scans for detecting COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID-19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 87.87 on the test set for the task of detecting the presence of COVID-19. This was the ‘runner-up’ for this task in the ‘AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition’ (MIA-COV19D). It achieved a macro f1 score of 46.00 for the task of classifying the severity of COVID-19 and was ranked in fourth place.

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Notes

  1. 1.

    The code for this project has been released under the Apache-2.0 license and is publicly available at https://github.com/rbturnbull/cov3d/.

  2. 2.

    TorchApp Framework is available as an alpha release at: https://github.com/rbturnbull/torchapp/.

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Acknowledgements

This research was supported by The University of Melbourne’s Research Computing Services and the Petascale Campus. Sean Crosby and Naren Chinnam were instrumental in arranging the computational resources necessary. I thank Patricia Desmond and David Turnbull for providing feedback on earlier versions of this article.

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Authors and Affiliations

  1. Melbourne Data Analytics Platform, University of Melbourne, Melbourne, VIC, 3060, Australia

    Robert Turnbull

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  1. Robert Turnbull
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Correspondence to Robert Turnbull .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Turnbull, R. (2023). Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13807. Springer, Cham. https://doi.org/10.1007/978-3-031-25082-8_45

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  • DOI: https://doi.org/10.1007/978-3-031-25082-8_45

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