Deep Learning Methods for Estimating "Brain Age" from Structural MRI Scans

Sebastian G. Popescu, James H. Cole, David J. Sharp, Ben Glocker

Apr 11, 2018 MIDL 2018 Abstract Submission readers: everyone
  • Abstract: Discrepancies between the chronological age of an individual and the neuroimaging based data driven "brain age" have been shown to be feasible biomarkers associated to a wide range of neurological disorders such as Alzheimer's Disease, traumatic brain injuries or psychiatric disorders. We devised a framework based on Deep Gaussian Processes which achieves state-of-the-art results in terms of global brain age prediction. We also introduced the first ever attempt of predicting brain age at voxel-level using context-sensitive Random Forests. The resulting models provide feasible brain-predicted age estimates for younger to middle-aged subjects, with less reliable estimates for older subjects.
  • Keywords: gaussian processes, random forest, deep learning, neuroimaging, healthy ageing, neurodegenerative diseases
  • Author affiliation: Imperial College London, King's College London
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