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

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

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Graph CNNs for population graphs

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README

        

Graph CNNs for population graphs: classification of the ABIDE dataset
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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}
}
```