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Author: awmulyadi

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+# Estimating Explainable Alzheimer's Disease Likelihood Map via Clinically-guided Prototype Learning
+![XADLiME](image/xadlime.png)
+
+This repository provides the PyTorch implementation of our proposed XProtoADPM 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
+```
+
+## Acknowledgements
+This work was supported by 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 (Artificial Intelligence Graduate School Program (Korea University)).