Conditional-Score-Based-Diffusion-Model-for-Cortical-Thickness-Trajectory-Prediction

This repository contains the source code associated with our paper titled "Conditional Score Based Diffusion Model for Cortical Thickness Trajectory Prediction" which has been accepted at MICCAI 2024.

Requirements

Ensure all the necessary packages listed.

numpy matplotlib scikit-learn scikit-image click requests psutil tqdm imageio imageio-ffmpeg pyspng pillow

Dataset

We evaluated our conditional score-based diffusion model using the Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge cohort (https://tadpole.grand-challenge.org/).

Example of data format is located under "https://github.com/siyeopyoon/Conditional-Score-Based-Diffusion-Model-for-Cortical-Thickness-Trajectory-Prediction/tree/main/Example%20Data%20Foramt"

Running the Training/Experiments

To conduct experiments, please build adn run docker image using the command below. Note that you should adjust the paths and hyperparameters according to your specific requirements:

1. move to the location of source code (where dockerfile is located).
2. Build docker image

sudo docker build -f ./dockerfile_train_residual -t model_train_residual ./

3. Run docker image

sudo docker run --shm-size=8G --rm --gpus all -v /home/example/:/external/ model_train_residual

Note; here "/home/example/" is where source code and dockerfile are located in your GPU server.

4. To perform experimentsn, please build adn run docker image using the command below.

sudo docker build -f ./dockerfile_generate_residual -t generate_residual ./

sudo docker run --shm-size=8G --rm --gpus all -v /home/example/:/external/ generate_residual

Pretrained model weights : https://drive.google.com/drive/folders/1MSyKmPCNtZ0z6cP2lIBEso0CdCXFVEF0?usp=sharing Please contact to author or leave the issue in github, if you have any question on model weights.