Copyright (C) 2022 Xu Tian (tianxu@csu.edu.cn), Jin Liu (liujin06@csu.edu.cn)
## Package Title:
3D MRI-based Multi-Task Learning for Alzheimer's Disease Diagnosis and MMSE Score Prediction: A Multi-site Validation
## Description:
This package is designed to enable automatic AD diagnosis and MMSE score prediction through multi-task interaction learning from structural magnetic resonance imaging (sMRI) brain scans.
![Framework of our proposed method](image/framework.png)
<p align="center">Fig. 1. Framework of our proposed method</p>
As is shown in Fig.1. , we propose an MRI-based multi-task interaction learning (MTIL) method for AD diagnosis and MMSE score prediction. Firstly, we implement both tasks using the same backbone. We insert three multi-task interaction layers between these two backbones. Each multi-task interaction layer consists of two feature decoupling modules and one feature sharing module. It takes interactive features of each task and interacts them to obtain shared representations for both tasks. Secondly, in order to further improve the consistency of the features selected by the feature decoupling module, we design a feature consistency loss to further constrain the module. Finally, in order to exploit the specific distribution information of MMSE scores in AD group and NC group, we design a distribution loss.
In this code, "config.json" places the model's hyperparameters and other information. "model.py" places the main CNN model. "model_wrapper.py" encapsulates the training, validation and testing process of the model. "model.py" encapsulates the main CNN model. "dataloader.py" encapsulates the data usage process required by the model. "loss.py" contains forward and backward computations for part of the model's loss.
## How to run this project:
This project must run in python>=3.6, The following steps should be taken to run this project:
1. Data preparation: Before the code can run, the data needs to be prepared. You need to put a file named "dataset.csv" in the "opencsv" folder. "dataset" is the name of the dataset in the configuration file. In this csv files, subjects' AD labels and MMSE scores should be placed in the second and third columns respectively.
To demonstrate this code, we provide data for 10 subjects from the ADNI1 dataset. These subjects were randomly selected from the test set of the experiments for the provided model files. These data are placed in the "data" folder and their label information is in the "opencsv" folder.
2. Environment building:
(1) Software: Information about the packages required by the code is at "requirements.txt".
(2) Hardware: This code has been tested with NVIDIA GTX2080.
3. Code running:
(1) Information about code running in "config.json" should be modified.
(2) You can use
python main.py
to complete the training and testing of the model, or use
python main.py train
python main.py test
to complete them separately.
Also, we put our trained model file in this code, it can be directly used for AD detection and MMSE score prediction. If you want to use it, execute the following command
python main.py valid
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阿尔茨海默病诊断和MMSE分数预测的3D MRI多任务学习模型内含数据集和环境运行说明.zip (24个子文件)
utils.py 5KB
loss.py 6KB
checkpoint_dir
valid
valid.pth 16.43MB
image
aaa 5B
framework.png 58KB
main.py 2KB
data
ADNI1
ADNI_128_S_0522_MR_MPR__GradWarp__N3__Scaled_2_Br_20081007100245322_S14792_I119400.npy 27.55MB
ADNI_005_S_0221_MR_MPR__GradWarp__B1_Correction__N3__Scaled_Br_20070910164745994_S11958_I72128.npy 27.55MB
ADNI_024_S_1307_MR_MPR__GradWarp__B1_Correction__N3__Scaled_Br_20070731173131780_S27061_I63415.npy 27.55MB
ADNI_100_S_0069_MR_MPR____N3__Scaled_Br_20061213163828351_S10563_I33105.npy 27.55MB
ADNI_099_S_0372_MR_MPR-R__GradWarp__B1_Correction__N3__Scaled_Br_20061228145622892_S13672_I34549.npy 27.55MB
ADNI_029_S_1056_MR_MPR-R__GradWarp__B1_Correction__N3__Scaled_Br_20070718151433161_S22977_I60741.npy 27.55MB
ADNI_057_S_0934_MR_MPR__GradWarp__B1_Correction__N3__Scaled_Br_20061229160134007_S19971_I34734.npy 27.55MB
ADNI_012_S_1133_MR_MPR____N3__Scaled_Br_20070711170922052_S25016_I59224.npy 27.55MB
ADNI_011_S_0008_MR_MPR-R__GradWarp__B1_Correction__N3__Scaled_Br_20061208113853608_S9195_I32264.npy 27.55MB
ADNI_002_S_1280_MR_MPR__GradWarp__B1_Correction__N3__Scaled_Br_20070713123810416_S26453_I60056.npy 27.55MB
model.py 9KB
lookupcsv
ADNI1.csv 1KB
model_wrapper.py 13KB
dataloader.py 3KB
requirements.txt 340B
config.json 590B
README.md 3KB
host 0B
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