Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis. - Wikidata - wikimedia - TriplyDB

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

http://www.wikidata.org/entity/Q33529022

about

Pattern recognition can detect subtle field defects in eyes of HIV individuals without retinitis under HAART.Patterns of functional vision loss in glaucoma determined with archetypal analysis Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.Improving our understanding, and detection, of glaucomatous damage: An approach based upon optical coherence tomography (OCT).

P2860

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P2860

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

http://www.wikidata.org/entity/Q33529022

description

2009 nî lūn-bûn

@nan

2009 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2009 թվականի դեկտեմբերին հրատարակված գիտական հոդված

@hy

2009年の論文

@ja

2009年論文

@yue

2009年論文

@zh-hant

2009年論文

@zh-hk

2009年論文

@zh-mo

2009年論文

@zh-tw

2009年论文

@wuu

name

Patterns of glaucomatous visua ...... ndependent component analysis.

@ast

Patterns of glaucomatous visua ...... ndependent component analysis.

@en

Patterns of glaucomatous visua ...... ndependent component analysis.

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type

label

Patterns of glaucomatous visua ...... ndependent component analysis.

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Patterns of glaucomatous visua ...... ndependent component analysis.

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Patterns of glaucomatous visua ...... ndependent component analysis.

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prefLabel

Patterns of glaucomatous visua ...... ndependent component analysis.

@ast

Patterns of glaucomatous visua ...... ndependent component analysis.

@en

Patterns of glaucomatous visua ...... ndependent component analysis.

@nl

P1433

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P1476

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P2860

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P304

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P31

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P478

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P577

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P698

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P921

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P932

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P1433

Transactions of the American Ophthalmological Society annual meeting

P1476

Patterns of glaucomatous visua ...... independent component analysis

@en

P2093

Chris Bowd

http://www.w3.org/2001/XMLSchema#string

Gil-Jin Jang

http://www.w3.org/2001/XMLSchema#string

Jeffrey Liebmann

http://www.w3.org/2001/XMLSchema#string

Jiucang Hao

http://www.w3.org/2001/XMLSchema#string

Linda M Zangwill

http://www.w3.org/2001/XMLSchema#string

Michael H Goldbaum

http://www.w3.org/2001/XMLSchema#string

Pamela A Sample

http://www.w3.org/2001/XMLSchema#string

Robert N Weinreb

http://www.w3.org/2001/XMLSchema#string

Tzyy-Ping Jung

http://www.w3.org/2001/XMLSchema#string

P2860

Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects

Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects

Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects

Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

A new generation of algorithms for computerized threshold perimetry, SITA.

Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components

Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields.

Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.

Interpretation of automated perimetry for glaucoma by neural network.

P304

136-144

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P478

107

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P50

P577

2009-12-01T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

20126490

http://www.w3.org/2001/XMLSchema#string

P921

independent component analysis

P932

2814563

http://www.w3.org/2001/XMLSchema#string