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Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields

PURPOSE: To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM–progression of patterns (POP) and VIM-POP). To compare GEM-...

詳細記述

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書誌詳細
出版年:Transl Vis Sci Technol
主要な著者: Yousefi, Siamak, Balasubramanian, Madhusudhanan, Goldbaum, Michael H., Medeiros, Felipe A., Zangwill, Linda M., Weinreb, Robert N., Liebmann, Jeffrey M., Girkin, Christopher A., Bowd, Christopher
フォーマット: Artigo
言語:Inglês
出版事項: The Association for Research in Vision and Ophthalmology 2016
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC4855479/
https://ncbi.nlm.nih.gov/pubmed/27152250
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1167/tvst.5.3.2
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