Self-supervised Longitudinal Neighbourhood Embedding (LNE), MICCAI 2021. paper
Self-supervised learning of neighborhood embedding for longitudinal MRI, Medical Image Analysis 2022. (LNE-v2) paper
conda env create -f requirement.yml
data_preprocessing_ADNI.py and data_preprocessing_LAB.py save images and other information in h5 files.
change parameters in config.yml (default setting is training LNE)
run python main.py
change parameters in config_v2.yml (default setting is training LNE-v2)
run python main_v2.py
change parameters in config.yml
For setting use_feature: ['z'], data_type: single, run python main_classification_single.py
For setting use_feature: ['z', 'delta_z'], data_type: pair, run python main_classification_pair.py
see visualization.ipynb for more details.