A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images [Paper]
Accepted at WACV2021 Conference
This repository contains the code to reproduce the experiments of the paper.
Installation
Datasets
COVID19
COVID19 V2
COVID19 V3
- https://zenodo.org/record/3757476#.XtU6wC2ZOuV (create directories: CT, Lung_Mask, Infection_Mask)
Reproducing paper experiments
Experiment hyperparameters are defined in ./exp_configs/weakly_exps.py
Run the following command to reproduce the experiments in the paper:
python trainval.py -e weakly_covid19_${DATASET}_${SPLIT} -sb ${SAVEDIR_BASE} -d ${DATADIR} -r 1
The variables (${...}
) can be substituted with the following values:
DATASET
(the COVID dataset):v1
,v2
, orv3
SPLIT
(the dataset split):mixed
,sep
SAVEDIR_BASE
: Absolute path to where results will be savedDATADIR
: Absolute path containing the downloaded datasets
Cite
@article{laradji2020weakly,
title={A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images},
author={Laradji, Issam and Rodriguez, Pau and Manas, Oscar and Lensink, Keegan and Law, Marco and Kurzman, Lironne and Parker, William and Vazquez, David and Nowrouzezahrai, Derek},
journal={arXiv preprint arXiv:2007.02180},
year={2020}
}