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Manifold Learning of Brain MRIs by Deep Learning
Tom Brosch1,3, Roger Tam2,3, and for the Alzheimer’s Disease Neuroimaging Initiative
1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
2Department of Radiology, University of British Columbia, Vancouver, Canada
3MS/MRI Research Group, University of British Columbia, Vancouver, Canada
Abstract. Manifold learning of medical images plays a potentially important role for modeling anatomical variability within a population with applications that include segmentation, registration, and prediction of clinical parameters. This paper describes a novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not require a predefined similarity measure or a prebuilt proximity graph. Our manifold learning method is based on deep learning, a machine learning approach that uses layered networks (called deep belief networks, or DBNs) and has received much attention recently in the computer vision field due to their success in object recognition tasks. DBNs have traditionally been too computationally expensive for application to 3D images due to the large number of trainable parameters. Our primary contributions are 1) a much more computationally efficient training method for DBNs that makes training on 3D medical images with a resolution of up to 128 × 128 × 128 practical, and 2) the demonstration that DBNs can learn a low-dimensional manifold of brain volumes that detects modes of variations that correlate to demographic and disease parameters.
Keywords: Manifold Learning, Machine Learning, Brain Imaging, MRI, Deep Learning, Deep Belief Networks
LNCS 8150, p. 633 ff.
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© Springer-Verlag Berlin Heidelberg 2013