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Independent component analysis (ICA), as an approach to the blindsource-separation (BSS) problem, has become the de-facto standard in manymedical imaging settings. Despite successes and a large ongoing researcheffort, the limitation of ICA to square linear transformations have not beenovercome, so that general INFOMAX is still far from being realized. As analternative, we present feature analysis in medical imaging as a problem solvedby Helmholtz machines, which include dimensionality reduction andreconstruction of the raw data under the same objective, and which recentlyhave overcome major difficulties in inference and learning with deep andnonlinear configurations. We demonstrate one approach to training Helmholtzmachines, variational auto-encoders (VAE), as a viable approach toward featureextraction with magnetic resonance imaging (MRI) data.

Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data