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Pamela A Sample

Showing results (81-90 of 104) with videos related to

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American Journal of Ophthalmology|January 15, 2008
Vision function in HIV-infected individuals without retinitis: report of the Studies of Ocular Complications of AIDS Research GroupWilliam R Freeman, Mark L Van Natta, Douglas Jabs, et al.
American Journal of Ophthalmology|August 29, 2006
Agreement and repeatability for standard automated perimetry and confocal scanning laser ophthalmoscopy in the diagnostic innovations in glaucoma studyDiana Ng, Linda M Zangwill, Lyne Racette, et al.
Investigative Ophthalmology & Visual Science|February 13, 2014
Detecting glaucoma progression from localized rates of retinal changes in parametric and nonparametric statistical framework with type I error controlMadhusudhanan Balasubramanian, Ery Arias-Castro, Felipe A Medeiros, et al.
Journal of Glaucoma|June 17, 2009
Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiersLyne Racette, Christine Y Chiou, Jiucang Hao, et al.
Investigative Ophthalmology & Visual Science|March 11, 2008
Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyesChristopher Bowd, Jiucang Hao, Ivan M Tavares, et al.
Investigative Ophthalmology & Visual Science|December 17, 2008
Comparing the full-threshold and Swedish interactive thresholding algorithms for short-wavelength automated perimetryMinna Ng, Lyne Racette, John P Pascual, et al.
Transactions of the American Ophthalmological Society|February 4, 2010
Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysisMichael H Goldbaum, Gil-Jin Jang, Chris Bowd, et al.
Journal of Glaucoma|October 12, 2011
African descent and glaucoma evaluation study: asymmetry of structural measures in normal participantsGrant H Moore, Christopher Bowd, Felipe A Medeiros, et al.
Investigative Ophthalmology & Visual Science|July 31, 2002
Using machine learning classifiers to identify glaucomatous change earlier in standard visual fieldsPamela A Sample, Michael H Goldbaum, Kwokleung Chan, et al.
Investigative Ophthalmology & Visual Science|July 28, 2004
Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defectsPamela A Sample, Kwokleung Chan, Catherine Boden, et al.
Pageof 11

Showing results (81-90 of 104) with videos related to

Sort By:
Pageof 11
American Journal of Ophthalmology|January 15, 2008
Vision function in HIV-infected individuals without retinitis: report of the Studies of Ocular Complications of AIDS Research GroupWilliam R Freeman, Mark L Van Natta, Douglas Jabs, et al.
American Journal of Ophthalmology|August 29, 2006
Agreement and repeatability for standard automated perimetry and confocal scanning laser ophthalmoscopy in the diagnostic innovations in glaucoma studyDiana Ng, Linda M Zangwill, Lyne Racette, et al.
Investigative Ophthalmology & Visual Science|February 13, 2014
Detecting glaucoma progression from localized rates of retinal changes in parametric and nonparametric statistical framework with type I error controlMadhusudhanan Balasubramanian, Ery Arias-Castro, Felipe A Medeiros, et al.
Journal of Glaucoma|June 17, 2009
Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiersLyne Racette, Christine Y Chiou, Jiucang Hao, et al.
Investigative Ophthalmology & Visual Science|March 11, 2008
Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyesChristopher Bowd, Jiucang Hao, Ivan M Tavares, et al.
Investigative Ophthalmology & Visual Science|December 17, 2008
Comparing the full-threshold and Swedish interactive thresholding algorithms for short-wavelength automated perimetryMinna Ng, Lyne Racette, John P Pascual, et al.
Transactions of the American Ophthalmological Society|February 4, 2010
Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysisMichael H Goldbaum, Gil-Jin Jang, Chris Bowd, et al.
Journal of Glaucoma|October 12, 2011
African descent and glaucoma evaluation study: asymmetry of structural measures in normal participantsGrant H Moore, Christopher Bowd, Felipe A Medeiros, et al.
Investigative Ophthalmology & Visual Science|July 31, 2002
Using machine learning classifiers to identify glaucomatous change earlier in standard visual fieldsPamela A Sample, Michael H Goldbaum, Kwokleung Chan, et al.
Investigative Ophthalmology & Visual Science|July 28, 2004
Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defectsPamela A Sample, Kwokleung Chan, Catherine Boden, et al.
Pageof 11