Assessing visual field clustering schemes using machine learning classifiers in standard perimetry. - Wikidata - wikimedia - TriplyDB

Assessing visual field clustering schemes using machine learning classifiers in standard perimetry.

Assessing visual field clustering schemes using machine learning classifiers in standard perimetry.

http://www.wikidata.org/entity/Q37108485

about

Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT.

P2860

Q30724196-40C39D81-B6EE-4419-8E0D-947C0C5FD1E9

P2860

Assessing visual field clustering schemes using machine learning classifiers in standard perimetry.

http://www.wikidata.org/entity/Q37108485

description

article científic

@ca

article scientifique

@fr

articolo scientifico

@it

artigo científico

@pt

bilimsel makale

@tr

scientific article published on December 2007

@en

vedecký článok

@sk

vetenskaplig artikel

@sv

videnskabelig artikel

@da

vědecký článek

@cs

name

Assessing visual field cluster ...... sifiers in standard perimetry.

@en

Assessing visual field cluster ...... sifiers in standard perimetry.

@nl

type

label

Assessing visual field cluster ...... sifiers in standard perimetry.

@en

Assessing visual field cluster ...... sifiers in standard perimetry.

@nl

prefLabel

Assessing visual field cluster ...... sifiers in standard perimetry.

@en

Assessing visual field cluster ...... sifiers in standard perimetry.

@nl

P1433

Q37108485-E6F9D22E-659A-425A-AE12-AE296B4BEF5D

P1476

Q37108485-F1293AB1-2EA5-4D6F-990F-27B2F1E91F01

P2093

Q37108485-0500995C-B93F-45B1-963A-9D38B303DB6C

Q37108485-229277C2-9D8C-4800-8BE2-D6917DA770E4

Q37108485-924A8780-5A5E-4446-B800-C9694A3D23FE

Q37108485-B49294C7-3443-4F29-BC6C-7981F03FD99C

Q37108485-CE22E9FA-2E5D-4039-9704-E0B86552F18B

P2860

Q37108485-0CBAF411-4F8C-4DF9-958E-4DA9000A1C52

Q37108485-116F7F57-CDE0-4D34-9A10-D66C1E9A829A

Q37108485-164FD5B8-EA4A-40C9-B66E-2E0510032963

Q37108485-392C9003-A0BC-4A34-AC05-4EFD800E04B6

Q37108485-3F651509-26F6-4923-91F8-3AB316D35B52

Q37108485-4348562B-9AE9-47D2-8D86-18FDF6A10C8B

Q37108485-45A012AF-80D8-491A-B637-5D576E222B2C

Q37108485-4AD73151-5F71-4CE4-B3F8-4A8051138137

Q37108485-4EDC82F9-4110-4107-B128-6CC9F61E16C0

Q37108485-5C6D5508-3EBD-44C4-A8B9-75BBE732583B

P304

Q37108485-DC5C2C9E-B358-4F55-B906-BB5F04905817

P31

Q37108485-7E061441-065B-41AF-8727-A1065556AFF1

P356

Q37108485-FB954307-7986-4EF3-BC8D-7514D4E59DB3

P407

Q37108485-CCDB2BDC-D6EB-4496-80E5-FCF76699B3F7

P433

Q37108485-CB6A03EC-C6C2-4E61-BC68-055708AED8F0

P478

Q37108485-2335C5B0-AFB6-4C21-A054-E29587BA5F7A

P50

Q37108485-0133066A-BC72-4171-93B7-85551B193DB0

Q37108485-81797CDE-4E9E-42AF-965B-4E856C591423

Q37108485-8DFDE54D-7329-4FFE-AE8B-FE4B138083E5

P577

Q37108485-3B3ACA67-F79A-4B43-AB43-7578AC906526

P5875

Q37108485-9512F4D5-FC93-4E60-9DB4-1AD220918091

P698

Q37108485-E89C1CCD-C7C9-4D39-B607-B6273578E37B

P921

Q37108485-C0254FB9-6334-4F7A-8E73-84B5EC0D3F2F

P932

Q37108485-FD7780C7-9716-493C-A0B4-2049E6A21F37

P356

P1433

Investigative Ophthalmology & Visual Science

P1476

Assessing visual field cluster ...... sifiers in standard perimetry.

@en

P2093

Jiucang Hao

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

Kwokleung Chan

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

Te-Wan Lee

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

P2860

Drug design by machine learning: support vector machines for pharmaceutical data analysis.

Diagnostic support for glaucoma using retinal images: a hybrid image analysis and data mining approach.

Effects of input data on the performance of a neural network in distinguishing normal and glaucomatous visual fields.

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

Mapping structural to functional damage in glaucoma with standard automated perimetry and confocal scanning laser ophthalmoscopy.

Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes.

Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.

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

Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography.

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

P304

5582-5590

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

P31

scholarly article

P356

10.1167/IOVS.06-0897

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

P407

P433

12

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

P478

48

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

P50

Catherine Boden

Robert N Weinreb

Linda M Zangwill

P577

2007-12-01T00:00:00Z

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

P5875

5791995

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

P698

18055807

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

P921

machine learning

P932

2647327

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