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We refer to a machine learning situation where models based on classicalconvolutional neural networks have shown good performance. We are investigatingdifferent encoding techniques in the form of supervoxels, then graphs to reducethe complexity of the model while tracking the loss of performance. Thisapproach is illustrated on a recognition task of resting-state functionalnetworks for patients with brain tumors. Graphs encoding supervoxels preserveactivation characteristics of functional brain networks from images, optimizemodel parameters by 26 times while maintaining CNN model performance.

Toward more frugal models for functional cerebral networks automatic recognition with resting-state fMRI