https://github.com/parisots/population-gcn

Graph CNNs for population graphs
https://github.com/parisots/population-gcn

Last synced: 3 months ago
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Graph CNNs for population graphs

• Host: GitHub
• URL: https://github.com/parisots/population-gcn
• Owner: parisots
• License: gpl-3.0
• Created: 2017-04-28T15:08:13.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2022-03-28T01:55:19.000Z (over 2 years ago)
• Last Synced: 2024-01-16T19:23:48.194Z (5 months ago)
• Language: Python
• Size: 34.2 KB
• Stars: 154
• Watchers: 8
• Forks: 71
• Open Issues: 6
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE.txt

Lists

• awesome-gcn - parisots/population-gcn

README

        
Graph CNNs for population graphs: classification of the ABIDE dataset

---------------------------------------------------------------------

 This code provides a python - Tensorflow implementation of graph convolutional networks (GCNs) for semi-supervised 

 disease prediction using population graphs, as described in: 

 

Parisot, S., Ktena, S. I., Ferrante, E., Lee, M., Moreno, R. G., Glocker, B., & Rueckert, D. (2017). 


[Spectral Graph Convolutions for Population-based Disease Prediction](https://arxiv.org/abs/1703.03020). 


MICCAI 2017.

and 

*Parisot, S., *Ktena, S. I., Ferrante, E., Lee, M., Moreno, R. G., Glocker, B., & Rueckert, D. (2017). 


[Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease](https://arxiv.org/pdf/1806.01738.pdf). 


Medical Image Analysis, 2018.

We provide an implementation applied to the [ABIDE dataset](preprocessed-connectomes-project.org/abide) 

for diagnosis of Autism Spectrum Disorder.

We also provide the list of scans from the [ADNI dataset](adni.loni.usc.edu/) used in our experiments. Each element of the list is in the format {SUBJECT_ID}_{ACQUISITION_MONTH}

#### INSTALLATION

To run the programme, you will need to install the implementation of graph convolutional networks (GCN) by Kipf et al.

This project is only compatible with our [forked GCN project](https://github.com/parisots/gcn).  

The root folder in fetch_data.py (line 12) and ABIDEParser.py (line 17) has to be updated to the folder were the data will be stored. 

Next, to install, organise and pre-process the ABIDE database:

python fetch_data.py 

#### USAGE

To run the programme with default parameters: 

 ```python

python main_ABIDE.py 

```

 

To get a detailed description of parameters:

 ```python

python main_ABIDE.py --help 

 ```

#### REQUIREMENTS 

tensorflow (>0.12) 


networkx 


nilearn 


scikit-learn 


joblib

#### REFERENCE 

Please cite our papers if you use this code in your own work:

```

@article{parisot2017spectral, 

  title={Spectral Graph Convolutions on Population Graphs for Disease Prediction}, 

  author={Parisot, Sarah and Ktena, Sofia Ira and Ferrante, Enzo and Lee, Matthew and Moreno, Ricardo Guerrerro and Glocker, Ben and Rueckert, Daniel}, 

  journal={MICCAI}, 

  year={2017} 

}

```

```

@article{parisot2018disease,

  title={Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease},

  author={Parisot, Sarah and Ktena, Sofia Ira and Ferrante, Enzo and Lee, Matthew and Guerrero, Ricardo and Glocker, Ben and Rueckert, Daniel},

  journal={Medical image analysis},

  year={2018},

  publisher={Elsevier}

}

```