This repo provides a Pytorch solution for predictions on X-ray images for COVID-19 patients.
It is intended to be used as a template for defeatcovid19 group partecipants who like to contribute. You can find more info on our group's effort here. At the moment we're actively trying to contact local hospitals to collect radiologic (mainly XRay and Eco) images to build a robust dataset for deep learning training.
The network of choice is ResNet34, provided by torchvision and pretrained on Imagenet. The net is first trained on the Kaggle Chest X-Ray Pneumonia dataset (5856 images) and then on the COVID-19 Chest X-Ray dataset (123 usable images).
Axial and lateral images were removed from the latter dataset. COVID-19 diagnoses were labelled 1, 0 otherwise (SARS/ARDS/Pneumocystis/Streptococcus/No finding).
An environment.yml file is provided to list the package requirements (mainly numpy, pandas, opencv, torch). The train entrypoint expects to find the aforementioned datasets in ./input. Adjust your paths accordingly.
You can train the network and see the results of the cross validation with
python train.py
The first part of the training (on the "Pneumonia" dataset) uses a simple 80/20 train/valid split. It achieves a ROC AUC score close to 1 for the selected fold. The second part of the training (on the "COVID" dataset) uses a more robust 5-fold cross validation and it results in a ~0.77 ROC AUC score.
- Paul Mooney, Chest X-Ray Images (Pneumonia), Kaggle dataset, https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia, 2018
- Joseph Paul Cohen, COVID-19 image data collection, https://github.com/ieee8023/covid-chestxray-dataset, 2020
This repo serves as a template for future effort of the defeatcovid19 group and as such is intended to be released under the MIT license.