This is the Pytorch implementation of the ECCV 2020 worshop paper "Dynamic Image for 3D MRI Image Alzheimer's Disease Classification". paper
Authors:Xin Xing, Gongbo Liang, Hunter Blanton, M. Usman Rafique, Chris Wang, Ai-Ling Lin, and Nathan Jacobs
The dynamic image python script is refered by this.
Offical Dynamic image link is here.
Corresponding author: Xin Xing (xtremexing@gmail.com)
The dataset is collected from ADNI, which is a public website. Here is the link of the data we used in our work.
Please save the MRI ".npy" data into CN and AD folders, respectively. You can use the "ADNI2_MRI_AD_niiData.ipynb" to convert the ".nii" file to ".npy" file.
The first operation is train-test files spliting. We are working on the 5-fold cross validation. So we use the "train_test_files_split.ipynb" to randomly split the data into 5 folders (Remember to change your data path). As following:
Then we can conduct the "Dynamic_image_Vgg11.ipynb" (Remember to change LABEL_PATH = '/data/scratch/xxing/adni_dl/Preprocessed/ADNI2_MRI' to your data path).
