Toward more frugal models for functional cerebral networks automatic
  recognition with resting-state fMRI
Lukman Ismaila Pejman Rasti Jean-Michel Lemée David Rousseau
We refer to a machine learning situation where models based on classical
convolutional neural networks have shown good performance. We are investigating
different encoding techniques in the form of supervoxels, then graphs to reduce
the complexity of the model while tracking the loss of performance. This
approach is illustrated on a recognition task of resting-state functional
networks for patients with brain tumors. Graphs encoding supervoxels preserve
activation characteristics of functional brain networks from images, optimize
model parameters by 26 times while maintaining CNN model performance.