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NEURO-DRAM: a 3D recurrent visual attention model for interpretable neuroimaging classification

Research output: Working paperPre-print

Original languageEnglish
Publication statusPublished - 18 Oct 2019

Publication series

NameAlzheimer’s Disease Neuroimaging Initiative

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King's Authors

Abstract

Deep learning is attracting significant interest in the neuroimaging community
as a means to diagnose psychiatric and neurological disorders from structural
magnetic resonance images. However, there is a tendency amongst researchers
to adopt architectures optimized for traditional computer vision tasks, rather than
design networks customized for neuroimaging data. We address this by introducing
NEURO-DRAM, a 3D recurrent visual attention model tailored for neuroimaging
classification with the flexibility to incorporate non-imaging information. The
model comprises an agent which, trained by reinforcement learning, learns to
navigate through volumetric images, selectively attending to the most informative
regions for a given task. When applied to Alzheimer’s disease prediction, NEURODRAM achieves state-of-the-art classification accuracy on an out-of-sample dataset,
significantly outperforming a baseline convolutional neural network. When further
applied to the task of predicting which patients with mild cognitive impairment
will be diagnosed with Alzheimer’s disease within two years, the model achieves
state-of-the-art accuracy with no additional training. Encouragingly, the agent
learns, without explicit instruction, a search policy in agreement with standardized
radiological hallmarks of Alzheimer’s disease, suggesting a route to automated
biomarker discovery for more poorly understood disorders.

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