2016/03/21 by Devon Hjelm, R. Devon Hjelm, Sergey M. Plis +6
Computer Science · Mathematics · #Blind Source Separation Techniques #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #cs.LG #cs.NE #stat.ML
paper · pdf · doi:10.48550/arxiv.1603.06624
arxiv created 2016/03/21 · openalex publication_date 2016/03/21 · arxiv updated 2016/03/23 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Independent component analysis (ICA), as an approach to the blind source-separation (BSS) problem, has become the de-facto standard in many medical imaging settings. Despite successes and a large ongoing research effort, the limitation of ICA to square linear transformations have not been overcome, so that general INFOMAX is still far from being realized. As an alternative, we present feature analysis in medical imaging as a problem solved by Helmholtz machines, which include dimensionality reduction and reconstruction of the raw data under the same objective, and which recently have overcome major difficulties in inference and learning with deep and nonlinear configurations. We demonstrate one approach to training Helmholtz machines, variational auto-encoders (VAE), as a viable approach toward feature extraction with magnetic resonance imaging (MRI) data.