XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning

This repository provides the PyTorch implementation of our proposed XADLiME framework in addressing Alzheimer's Disease progression modeling.

Datasets

We utilized Alzheimer's disease neuroimaging initiative dataset

http://www.loni.usc.edu/ADNI

Usage

ADPEN

For pretraining the ADPEN, run:

python xadlime_adpen.py --fold=1 --gpu_id=0 --finetune=0

For finetuning the ADPEN, make a list the pretrained directory location and run:

python xadlime_adpen.py --fold=1 --gpu_id=0 --finetune=1

ProgAE

For training the autoencoder for progression map, run:

python xadlime_progae.py --fold=1 --gpu_id=0

XADLiME

After training all required networks, XADLiME can be executed through:

python xadlime_classification_clinicalstage.py --fold=1 --gpu_id=0

python xadlime_regression_mmse.py --fold=1 --gpu_id=0

python xadlime_regression_age.py --fold=1 --gpu_id=0

Citation

If you find this work useful for your research, please cite our preprint paper.

@misc{mulyadi2022xadlime,
  doi={10.48550/ARXIV.2207.13223},
  url={https://arxiv.org/abs/2207.13223},
  author={Mulyadi, Ahmad Wisnu and Jung, Wonsik and Oh, Kwanseok and Yoon, Jee Seok and Suk, Heung-Il},
  title={XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning},
  publisher={arXiv},
  year={2022},
  copyright={arXiv.org perpetual, non-exclusive license}
}

or our recently published journal version in NeuroImage.

@article{MULYADI2023120073,
title = {Estimating explainable Alzheimer’s disease likelihood map via clinically-guided prototype learning},
journal = {NeuroImage},
volume = {273},
pages = {120073},
year = {2023},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2023.120073},
url = {https://www.sciencedirect.com/science/article/pii/S1053811923002197},
author = {Ahmad Wisnu Mulyadi and Wonsik Jung and Kwanseok Oh and Jee Seok Yoon and Kun Ho Lee and Heung-Il Suk},
keywords = {Alzheimer’s Disease, Explainable AI, Prototype Learning},
}

Acknowledgements

This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) No. 2022-0-00959 ((Part 2) Few-Shot Learning of Causal Inference in Vision and Language for Decision Making) and No. 2019-0-00079 (Department of Artificial Intelligence (Korea University)). This study was further supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2022R1A4A1033856) and KBRI basic research program through Korea Brain Research Institute funded by the Ministry of Science and ICT (22-BR-03-05).

ku-milab/XADLiME