Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes.
about
Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT.Spectral domain optical coherence tomography in glaucoma: qualitative and quantitative analysis of the optic nerve head and retinal nerve fiber layer (an AOS thesis)Evaluation of a combined index of optic nerve structure and function for glaucoma diagnosis.Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiersIntegration and fusion of standard automated perimetry and optical coherence tomography data for improved automated glaucoma diagnostics.Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurementsOn improving the use of OCT imaging for detecting glaucomatous damageCombining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma DetectionPopulation-based evaluation of retinal nerve fiber layer, retinal ganglion cell layer, and inner plexiform layer as a diagnostic tool for glaucomaGlaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiersAutomated classifiers for early detection and diagnosis of retinopathy in diabetic eyes.Combining information from 3 anatomic regions in the diagnosis of glaucoma with time-domain optical coherence tomography.Improving glaucoma detection using spatially correspondent clusters of damage and by combining standard automated perimetry and optical coherence tomographyDevelopment and validation of an improved neurological hemifield test to identify chiasmal and postchiasmal lesions by automated perimetry.Machine Learning Techniques in Clinical Vision Sciences.Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology.Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects.Diagnostic ability of Humphrey perimetry, Octopus perimetry, and optical coherence tomography for glaucomatous optic neuropathy.
P2860
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P2860
Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes.
description
2008 nî lūn-bûn
@nan
2008 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի մարտին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@ast
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@en
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@nl
type
label
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@ast
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@en
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@nl
prefLabel
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@ast
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@en
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@nl
P2093
P50
P356
P1476
Bayesian machine learning clas ...... healthy and glaucomatous eyes.
@en
P2093
Felipe A Medeiros
Jiucang Hao
Michael H Goldbaum
Pamela A Sample
Te-Won Lee
P304
P356
10.1167/IOVS.07-1083
P407
P577
2008-03-01T00:00:00Z