Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.
about
Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurementsGlaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiersDetecting glaucomatous change in visual fields: Analysis with an optimization frameworkUnsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.Development and validation of an improved neurological hemifield test to identify chiasmal and postchiasmal lesions by automated perimetry.Machine Learning Techniques in Clinical Vision Sciences.Recognizing patterns of visual field loss using unsupervised machine learning.Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.
P2860
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P2860
Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.
description
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name
Progression of patterns (POP): ...... progression in visual fields.
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Progression of patterns (POP): ...... progression in visual fields.
@en
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Progression of patterns (POP): ...... progression in visual fields.
@ast
Progression of patterns (POP): ...... progression in visual fields.
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Progression of patterns (POP): ...... progression in visual fields.
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Progression of patterns (POP): ...... progression in visual fields.
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P2093
P2860
P50
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P1476
Progression of patterns (POP): ...... progression in visual fields.
@en
P2093
Christopher A Girkin
Christopher Bowd
Douglas R Anderson
Felipe A Medeiros
Giljin Jang
Jeffrey M Liebmann
Madhusudhanan Balasubramanian
Marie-Josee Fredette
Michael H Goldbaum
P2860
P304
P356
10.1167/IOVS.11-8363
P407
P577
2012-09-25T00:00:00Z