Disease Disentanglement GAN (DiDiGAN)

This is the code for the MICCAI 2023 paper Style-Based Manifold for Weakly-Supervised Disease Characteristic Discovery.

Data

• ADNI dataset (requires registration)
• AD/CN labels are provided in ./data/*.csv

Preprocessin

• Extract the center 40 coronal slices from each 3D image.
• Zero-pad each image to 256 x 256
• Re-normalise image pixel values using cv2 to [0-255]
• Save files as png. Save all the AD images in one folder (e.g. ./dataset/adni/ad/{unique-name}.png) and all the CN images in another folder (e.g. ./dataset/adni/cn/{unique-name}.png).

How to run

• Build Docker image as defined by ./Dockerfile
• Run pip install umap-learn using a container.
• Run python main.py --conf training/ce_2class/config.json

Note: before running, update the data location in the config.json files:

...
    "training_set_kwargs": {
        "class_name": "training.dataset.ADNIDataset",
        "path": "dataset/adni",
        "constraint_res": 64
    },
...

TODO:

• New docker container to include the umap-learn package
• More detailed code documentation.

Citation

@InProceedings{10.1007/978-3-031-43904-9_36,
author="Liu, Siyu
and Liu, Linfeng
and Engstrom, Craig
and To, Xuan Vinh
and Ge, Zongyuan
and Crozier, Stuart
and Nasrallah, Fatima
and Chandra, Shekhar S.",
editor="Greenspan, Hayit
and Madabhushi, Anant
and Mousavi, Parvin
and Salcudean, Septimiu
and Duncan, James
and Syeda-Mahmood, Tanveer
and Taylor, Russell",
title="Style-Based Manifold for Weakly-Supervised Disease Characteristic Discovery",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="368--378",
}