A deep learning model to detect the presence and severity of COVID19 in patients from CT-scans. It has been submitted as part of the workshop 'AI-enabled Medical Image Analysis – Digital Pathology & Radiology/COVID19 (MIA-COV19D)' at the European Conference on Computer Vision (ECCV) in 2022 and the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) in 2023.
Warning
This project is part of a computer vision competition and has not been released for clinical use.
The following is a quick introduction to running cov3d. For more detailed information, see the documentation and the accompanying papers.
Install cov3d with pip:
pip install git+https://github.com/rbturnbull/cov3d.git
To train cov3d, use this command:
cov3d train --directory ./scans
The training data and the two CSV files are part of the COV19-CT-DB Database which is available from MIA-COV19D workshop team.
The default hyperparameters for training can be overridden using the command-line interface. All options for training the model can be seen with the command:
cov3d train --help
Also see the training options in the Command Line Interface documentation.
If you have a trained model, you can infer the presence and severity of COVID19 from a CT scan with the command:
cov3d infer --pretrained <path/to/model> --scan <path/to/ct-scan>
All options for inference can be found with the command:
cov3d infer --help
Also see the inference options in the Command Line Interface documentation.
- Robert Turnbull <robert.turnbull@unimelb.edu.au>
- Created using torchapp (https://github.com/rbturnbull/torchapp)