Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows identification functional networks to preserve patient’s neurological functions. One fMRI technique used identify network resting-state-fMRI (rs-fMRI). This not routinely available because necessity have expert reviewer who can manually each network. The lack sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools full automation this clinical task. In article, we investigate possibility such via transfer method from healthy control patient boost detection in rs-fMRI data. end-to-end deep model implemented article distinguishes seven principal using images. best performance 75% correct recognition rate obtained proposed architecture, shows its superiority over other algorithms that were equally tested classification Based reference model, demonstrate boosting results our algorithm with patients patients. application opens interesting possibilities subjects be easily enrolled acquisition since it non-invasive. Consequently, process helps compensate usual small cohort could extended medical imaging modalities and pathology.