This is the official implementation of Visual-Attribute Prompt Learning for Progressive Mild Cognitive Impairment Prediction at MICCAI-2023
conda create -n VAPFormer python=3.7
conda activate VAPFormer
pip install -r requirements.txt
The data can be accessed at http://adni.loni.usc.edu/data-samples/access-data/ You can use SPM12,CAT12 to preprocess data
Task | Link |
---|---|
AD v.s. NC | OneDrive |
sMCI v.s. pMCI | OneDrive |
Modify the code in trainclinical.py
with the following two lines of code
ld_helper = LoaderHelper(task=Task.NC_v_AD)
# which defineds the task
model_uuid = train_camull(ld_helper, epochs=50)
# function to train the model
The weights are stored in the weights file
Modify the code in train_modelclinical.py as follows:
ld_helper = LoaderHelper(task=Task.sMCI_v_pMCI)
model = load_model()
# Modify the pretrained weight
model_uuid = train_camull(ld_helper, model=model, epochs=50)
evaluate_model(DEVICE, model_uuid, ld_helper)
This project is built upon the foundations of several open-source codebases, including camull-net and VPT.