Multi-View GCN (MVGCN) for Brain Networks

Overview

This repository contains TensorFlow code for implementing Multi-View Graph Convolutional Network for brain networks (DTI data). To do this, we train models using graph convolutional networks (GCNs) in multiple views to learn view-based feature representations. Then, view pooling is conducted for the purpose of multi-view feature fusion. The code is documented and should be easy to modify for your own applications.

Model

The objective function is established for a binary classification problem, which is matching vs. non-matching classes. Brain networks in the same group (Parkinson's Disease or Healthy Control) are labeled as matching pairs while brain networks from different groups are labeled as non-matching pairs. Hence, pairwise training samples are feed into the neural network. The figure depicts the pairwise learning architecture.

• The components utilized in the MVGCN are:
  • GCNs: incorporating coordinates of ROI (Region of Interest) with brain networks (DTI) together to learn interpretable representations.
  • View pooling: combining view-based representations of DTI data that are obtained from various tractography algorithms.
  • Pairwise matching: computing similarities by euclidean distance for sample pairs in terms of the pooled features.
  • Softmax: classifying the output of pairwise matching layer into the matching vs. non-matching classes.

This repository contains the slides we presented in AMIA 2018.

Requirements

This package has the following requirements:

• An NVIDIA GPU.
• Python 3.x
• TensorFlow 1.4

Usage

How to Run

To run MVGCN on your data, you need to: change the function of loading data in utils.py; define the names of multiple views in mvgcn.sh; set hyperparameters for MVGCN in mvgcn.sh; run the shell script mvgcn.sh