An Explainable Lesion-guided Multi-task Learning For Alzheimer;s Disease Diagnosis

Flowchart of our DeepTAAD

Classification result of our method

The roc curve of our model are shown as follow:

Requirements

python 3.7
pytorch 1.6.0
torchvision 0.7.0
pickle
nibabel
setproctitle
medcam
medpy

Training

Run the training script on the ADNI dataset. Distributed training is available for training the proposed DeepTAAD, where --nproc_per_node decides the numer of gpus and --master_port implys the port number. you can use the following command to train the model with task assistance loss CUDA_VISIBLE_DEVICES=0 python3 -m torch.distributed.launch --nproc_per_node=1 --master_port 20003 train.py

Testing

If you want to test the model which has been trained on the ADNI dataset, run the testing script as following. python test_ADNI.py

LeoLjl / Explainable-Alzheimer-s-Disease-Diagnosis