Abstract
Deep learning, with its ability to automatically extract hierarchical feature sets from large datasets, has produced breakthrough results in many computer vision applications, and has the potential to transform neuroimage analysis. However, 3D brain images pose unique challenges due to their complex content and high dimensionality relative to the typical number of images available, making optimization of deep networks and evaluation of extracted features difficult. This chapter reviews the most popular models used in deep learning in computer vision, from restricted Boltzmann machines to convolutional neural networks, and summarizes the literature of deep learning applications in neuroimaging. There is a special focus on deep learning for the study of multiple sclerosis, a neurological disease with complex pathology and heterogeneous radiological features.
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