Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks

Environment installation:

• Once that's done, create a virtual environment with make_virtual_env.sh
• Activate the environment with source multitask_method_env/bin/activate
• Set paths for input/output files in multitask_method/paths.py

Alternatively, you can use the .devcontainer to run the code in a docker container which creates a virtual environment from the requirements.txt file.

Data

The HCP dataset is available at https://www.humanconnectome.org/study/hcp-young-adult

The BraTS 2017 dataset is available at https://www.med.upenn.edu/sbia/brats2017/registration.html

The ISLES 2015 dataset is available at https://www.smir.ch/ISLES/Start2015

The VinDr-CXR dataset is available at https://physionet.org/content/vindr-cxr/1.0.0/

Inter-dataset blending datasets

The MOOD dataset (for 3D inter-dataset blending) is available at https://www.synapse.org/#!Synapse:syn21343101/wiki/599515

The ImageNET dataset (for 2D inter-dataset blending) is available at https://www.kaggle.com/c/imagenet-object-localization-challenge/overview

Preprocessing

Preprocessing scripts are available in the multitask_method/preprocessing folder. Don't forget to set the paths for preprocessed data and predictions to be saved in multitask_method/paths.py

Training

With the environment activated, run python train.py ABSOLUTE_PATH_TO_EXPERIMENT_CONFIG FOLD_NUMBER

Experiment configs used for the paper are in the experiments folder.

Prediction and Evaluation

To generate predictions on the test set, run python predict.py ABSOLUTE_PATH_TO_EXPERIMENT_CONFIG.

To evaluate the predictions at the normal resolution, run python eval.py ABSOLUTE_PATH_TO_EXPERIMENT_CONFIG.

Result metrics are saved in the predictions folder as results.json.

To evaluate the predictions at CRADL's resolution, run python cradl_eval.py ABSOLUTE_PATH_TO_EXPERIMENT_CONFIG.

Reproducibility

Brain

Download the datasets from the above links Run the brain preprocessing script multitask_method/preprocessing/brain_preproc.py

When experimenting with training on T tasks, run:

For F in range(0, 5CT) run python train.py <path_to_repo>/experiments/exp_HCP_low_res_T_train.py F To produce predictions run: python predict.py <path_to_repo>/experiments/exp_HCP_low_res_T_train.py F To evaluate the predictions run: python eval.py <path_to_repo>/experiments/exp_HCP_low_res_T_train.py F

VinDr-CXR

Download the datasets from the above links Run the brain preprocessing script multitask_method/preprocessing/vindr_cxr_preproc.py When experimenting with training on T tasks, run: For F in range(0, 5CT) run python train.py <path_to_repo>/experiments/exp_VINDR_low_res_T_train.py F To produce predictions run: python predict.py <path_to_repo>/experiments/exp_VINDR_low_res_T_train.py F To evaluate the predictions run: python eval.py <path_to_repo>/experiments/exp_VINDR_low_res_T_train.py F