Using Graph Convolutional Networks to Address fMRI Small Data Problems
This addresses the problem of limited data in medical imaging prognosis for researchers and clinicians, though it is incremental as it builds on existing graph neural network techniques.
The paper tackled the challenge of learning from small fMRI datasets for predicting treatment responses by using graph convolutional networks on connectivity graphs, achieving approximately 12% improvement over traditional deep learning methods.
Although great advances in the analysis of neuroimaging data have been made, a major challenge is a lack of training data. This is less problematic in tasks such as diagnosis, where much data exists, but particularly prevalent in harder problems such as predicting treatment responses (prognosis), where data is focused and hence limited. Here, we address the learning from small data problems for medical imaging using graph neural networks. This is particularly challenging as the information about the patients is themselves graphs (regions of interest connectivity graphs). We show how a spectral representation of the connectivity data allows for efficient propagation that can yield approximately 12\% improvement over traditional deep learning methods using the exact same data. We show that our method's superior performance is due to a data smoothing result that can be measured by closing the number of triangle inequalities and thereby satisfying transitivity.