SHRED

This repo contains the code for various variants of the SHRED architecture.

A previous version of the repo, linked to our MICCAI 2022 submission, can be found on the branch miccai_2022.

Data is available for download at the following links: SchizConnect, UCLA. Some sites in SchizConnect seem to be down for some time.

Environment Setup

1. Create and activate new conda environment

    conda create -n <env_name> python=3.8
    conda activate <env_name>
   

2. Run setup.sh

    chmod u+x ./setup.sh
    ./setup.sh
   

Setup for a new dataset

1. Prepare dataset

   • Create a new folder under ./src with the dataset name (see ./Schiz for reference) and modify the setup and config files.
   • Edit __init__.py to specify how to retrieve site, age and gender. Labelling standards too, if applicable.
   • Add dataset to DataloaderBase class (_get_indices() too) and Dataset class in ./src/data.py

2. Create .yml files in config_template to define model hyperparameters used and training settings. More details about the YAML files can be found in the miccai_2022 branch.

3. Train the model (and any other models - specify in the .yml file) using single_stage_framework.py.

    python single_stage_framework.py --config config_templates/individual/SHRED-III.yml