A PyTorch implementation of "Multi-Size Computer-Aided Diagnosis of Positron Emission Tomography Images Using Graph Convolutional Networks" (ISBI 2019).
Convolutional Neural Network (CNN) has shown superior accuracy and generalizability in performing prediction tasks, thanks to its good utilization of the grid-like structure of input images in Euclidean space. In this work we propose the Deep Voxel-Graph Convolution Network (DVGCN). DVGCN represents input images as their affinity graph and performs graph convolution to extract discriminative features. It then utilizes a sortpooling layer to sort the nodes in the graph and unifies the feature size used for prediction across images.
The codebase is implemented in Python 2.7.3. package versions used for development are just below.
networkx 1.11
torch 0.4.1
numpy 1.15.4
pandas 0.23.4
scipy 1.1.0
argparse 1.1.0
tensorflow 1.1.0
h5py 2.5.0
scikit-learn 0.20.3
We compare DVGCN with 3D-CNN and traditional radiomics models.
Results on AJCC Staging label:
| Method | Accuracy | Sensitivity | Specificity |
|---|---|---|---|
| Radiomics | 0.821 | 0.662 | 0.981 |
| 3D-CNN | 0.711 | 0.642 | 0.779 |
| DVGCN | 0.837 | 0.861 | 0.813 |
First compile s2v_lib then run the main.py
cd s2v_lib
make -j4
cd ..
python main.py@inproceedings{zhao2019multi,
title={Multi-Size Computer-Aided Diagnosis Of Positron Emission Tomography Images Using Graph Convolutional Networks},
author={Zhao, Xuandong and Li, Xiang and Guo, Ning and Zhou, Zhiling and Meng, Xiaxia and Li, Quanzheng},
booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
pages={837--840},
year={2019},
organization={IEEE}
}