Instance Importance-aware Graph Convolutional Network (I²GCN)

This repository is an official PyTorch implementation of the paper "Instance Importance-Aware Graph Convolutional Network for 3D Medical Diagnosis" [paper] from Medical Image Analysis 2022.

I²GCN for 3D Medical Diagnosis

• Considering the high cost of collecting exhaustive annotations for 3D data, a sustainable alternative is to develop diagnosis algorithms with merely patient-level labels. We propose the Instance Importance-aware Graph Convolutional Network (I²GCN) under the multi-instance learning (MIL).
• Using a preliminary MIL classifier, we first calculate the instance importance of each slice towards diagnosis. In the refined diagnosis branch, we devise the Instance Importance-aware Graph Convolutional Layer (I²GCLayer) to exploit complementary features in both importance-based and feature-based topologies. Moreover, the importance-based Sub-Graph Augmentation (SGA) is devised to alleviate the deficient supervision of 3D dataset.

Download

The processed CC-CCII dataset can be downloaded from Google Drive. Put the downloaded .npy files in a newly-built folder ./data/. Please note that among the three-fold cross-validation with random split, the performance of split1 and split2 is slightly higher than the split0.

Dependencies

• Python 3.6
• PyTorch >= 1.3.0
• numpy 1.19.4
• scikit-learn 0.24.2
• scipy 1.3.1

Code

Clone this repository into any place you want.

git clone https://github.com/CityU-AIM-Group/I2GCN.git
cd I2GCN
mkdir experiment; mkdir data

Quickstart

• Train the I²GCN with default settings:

python ./main.py --theme default --test_split 1 --online_flag 1

We provide the dataloader with two ways of loading npy files, including online and offline.

Cite

If you find our work useful in your research or publication, please cite our work:

@article{CHEN2022102421,
	title = {Instance Importance-Aware Graph Convolutional Network for 3D Medical Diagnosis},
	author = {Zhen Chen and Jie Liu and Meilu Zhu and Peter Y.M. Woo and Yixuan Yuan},
	journal = {Medical Image Analysis},
	pages = {102421},
	year = {2022},
	issn = {1361-8415},
	doi = {https://doi.org/10.1016/j.media.2022.102421}
}

Acknowledgements

• CC-CCII dataset from China National Center for Bioinformation, the largest public COVID-19 dataset of 3D lung CT scans until publication.