Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects (Q33224000)

24 July 2017

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

1 reference

independent component analysis

24 July 2017

unsupervised learning

1 reference

based on heuristic

inferred from title

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based on heuristic

inferred from title

author

Terrence J. Sejnowski

11

Terrence Sejnowski

0 references

Catherine Boden

Catherine Boden

7

0 references

Christopher Bowd

Christopher Bowd

8

0 references

Robert N Weinreb

13

Robert N Weinreb

1 reference

24 July 2017

10

Linda Zangwill

1 reference

24 July 2017

2

Pamela A Sample

1 reference

24 July 2017

1

Michael H Goldbaum

1 reference

24 July 2017

9

Rupert Bourne

1 reference

24 July 2017

Zuohua Zhang

3

1 reference

24 July 2017

Kwokleung Chan

4

1 reference

24 July 2017

Jiucang Hao

5

1 reference

24 July 2017

Te-Won Lee

6

1 reference

24 July 2017

David Spinak

12

1 reference

24 July 2017

language of work or name

English

0 references

publication date

1 October 2005

1 reference

Investigative Ophthalmology Visual Science

24 July 2017

1 reference

24 July 2017

46

1 reference

24 July 2017

10

1 reference

24 July 2017

3676-3683

1 reference

Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components

24 July 2017

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

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

14 July 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

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

14 July 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc

14 July 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

Spatial classification of glaucomatous visual field loss

14 July 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

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

14 July 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

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

31 October 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

Neural networks to identify glaucoma with structural and functional measurements.

31 October 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

Interpretation of automated perimetry for glaucoma by neural network

31 October 2018

1 reference

https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pmc&linkname=pmc_refs_pubmed&retmode=json&id=1866286

Assessing the utility of reliability indices for automated visual fields. Testing ocular hypertensives

31 October 2018

1 reference

PubMed

https://pubmed.ncbi.nlm.nih.gov/16186349

inferred from PubMed ID database lookup

12 December 2020

based on heuristic

Identifiers

DOI

10.1167/IOVS.04-1167

1 reference

24 July 2017

1 reference

24 July 2017

1 reference