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https://github.com/parisots/population-gcn
Graph CNNs for population graphs
https://github.com/parisots/population-gcn
Last synced: 14 days 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 (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-03-28T01:55:19.000Z (over 2 years ago)
- Last Synced: 2024-08-02T08:10:15.545Z (4 months ago)
- Language: Python
- Size: 34.2 KB
- Stars: 168
- Watchers: 8
- Forks: 71
- Open Issues: 6
-
Metadata Files:
- Readme: readme.md
- License: LICENSE.txt
Awesome Lists containing this project
- 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}
}
```