Balanced Graph Structure Information for Brain Disease Detection
Falih Gozi Febrinanto Mujie Liu Feng Xia
Analyzing connections between brain regions of interest (ROI) is vital to
detect neurological disorders such as autism or schizophrenia. Recent
advancements employ graph neural networks (GNNs) to utilize graph structures in
brains, improving detection performances. Current methods use correlation
measures between ROI's blood-oxygen-level-dependent (BOLD) signals to generate
the graph structure. Other methods use the training samples to learn the
optimal graph structure through end-to-end learning. However, implementing
those methods independently leads to some issues with noisy data for the
correlation graphs and overfitting problems for the optimal graph. In this
work, we proposed Bargrain (balanced graph structure for brains), which models
two graph structures: filtered correlation matrix and optimal sample graph
using graph convolution networks (GCNs). This approach aims to get advantages
from both graphs and address the limitations of only relying on a single type
of structure. Based on our extensive experiment, Bargrain outperforms
state-of-the-art methods in classification tasks on brain disease datasets, as
measured by average F1 scores.