NEURO-DRAM: a 3D recurrent visual attention model for interpretable
neuroimaging classification
release_dw34mocxcvabflmuaim4gt2wiu
by
David Wood, James Cole, Thomas Booth
2019
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. 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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