ML Anthology

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

Robert Turnbull
ECCVW 2022 pp. 663-676
doi:10.1007/978-3-031-25082-8_45 /eccvw/2022/turnbull2022eccvw-using/

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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Text

Turnbull. "Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25082-8_45

Markdown

[Turnbull. "Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/turnbull2022eccvw-using/) doi:10.1007/978-3-031-25082-8_45

BibTeX

@inproceedings{turnbull2022eccvw-using,
  title     = {{Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans}},
  author    = {Turnbull, Robert},
  booktitle = {European Conference on Computer Vision Workshops},
  year      = {2022},
  pages     = {663-676},
  doi       = {10.1007/978-3-031-25082-8_45},
  url       = {https://mlanthology.org/eccvw/2022/turnbull2022eccvw-using/}
}