Gilad Deutch, Eran Levin
pip install requirements.txtIn order to recreate this experiment you first to build the datasets "cocktail". Download the following datasets -
https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database?resource=download
https://www.kaggle.com/datasets/andyczhao/covidx-cxr2
https://paperswithcode.com/dataset/chestx-ray14
https://www.nature.com/articles/sdata2018161
https://www.med.upenn.edu/cbica/brats2020/data.html
Run the file datasets/separate_train_test.py to separate the chestx-ray14 data into train, test, validation1 and validation2 datasets.
python mae_training.py --save_model_name "some model name.pth"...python classification_training.py --pretrained_path "some model name.pth" --save_model_name "some other model name.pth"...python test_set_evaluation.py --pretrained_path "some other model name.pth" ...Note that this repo works with wandb, a link should be outputted in the start of each run (mae/classification/test), click to view run metrics.
For sweeping see init_classification_sweep.py and init_mae_sweep.py for classification and mae respectively. Also, run_classification_sweep.py and run_mae_sweep.py for running the sweeps. We use wandb's sweeping feature.
compute_dataset_mean_std.py computes the mean and std for a dataset, used for normalization.
utils.py contains implementation for our Dataset class.
datasets/analyse_datasets.py prints a summary of each dataset, its size and image type and dimensions.