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Downloading Clinical Data

The MIMICNoFinding, CheXpertNoFinding, CXRMultisite, and MIMICNotes datasets are not able to be downloaded through scripts/download.py as they require additional steps to gain access.

MIMICNoFinding

  1. Obtain access to the MIMIC-CXR-JPG Database on PhysioNet and download the dataset. We recommend downloading from the GCP bucket:
gcloud auth login
mkdir MIMIC-CXR-JPG
gsutil -m rsync -d -r gs://mimic-cxr-jpg-2.0.0.physionet.org MIMIC-CXR-JPG

  1. In order to obtain demographic information for each patient, you will need to obtain access to MIMIC-IV. Download core/patients.csv.gz and core/admissions.csv.gz and place the files in the MIMIC-CXR-JPG directory.

  2. Move or create a symbolic link to the MIMIC-CXR-JPG folder from your data directory.

  3. Run python -m subpopbench.scripts.download mimic_cxr --data_path <data_path>.

  4. (Optional) As the original jpgs have very high resolution, caching the images as downsampled copies might speed things up if you are training a lot of models. In this case, you should run python -m subpopbench.scripts.cache_mimic_cxr --data_path <data_path>.

CheXpertNoFinding

  1. Download the downsampled CheXpert dataset and extract it.

  2. Register for an account and download the CheXpert demographics data here. Place the CHEXPERT DEMO.xlsx in your CheXpert directory.

  3. Move or create a symbolic link to the CheXpert-v1.0-small folder named chexpert in your data directory.

  4. Run python -m subpopbench.scripts.download chexpert --data_path <data_path>.

CXRMultisite

  1. Acquire both of the datasets above.

  2. Run python -m subpopbench.scripts.download cxr_multisite --data_path <data_path>.

MIMICNotes

  1. Obtain access to the MIMIC-III Database on PhysioNet.

  2. Follow the instructions here to load MIMIC-III into a PostgreSQL database.

  3. Modify scripts/preprocess_mimic_notes.py to update output_dir with your data directory, as well as the database access credentials. Then, run python -m subpopbench.scripts.preprocess_mimic_notes.

  4. Run python -m subpopbench.scripts.download mimic_notes --data_path <data_path>.