- 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.
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/
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 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
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.
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.
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
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
@InProceedings{baugh2023manytasks,
author="Baugh, Matthew
and Tan, Jeremy
and M{\"u}ller, Johanna P.
and Dombrowski, Mischa
and Batten, James
and Kainz, Bernhard",
title="Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
editor="Greenspan, Hayit
and Madabhushi, Anant
and Mousavi, Parvin
and Salcudean, Septimiu
and Duncan, James
and Syeda-Mahmood, Tanveer
and Taylor, Russell",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="162--172",
isbn="978-3-031-43907-0"
}
(Some) HPC resources were provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR projects b143dc and b180dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) – 440719683.