Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements
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Detecting glaucomatous change in visual fields: Analysis with an optimization frameworkUnsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.Machine Learning Techniques in Clinical Vision Sciences.Application of Pattern Recognition Analysis to Optimize Hemifield Asymmetry Patterns for Early Detection of Glaucoma
P2860
Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements
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2014 nî lūn-bûn
@nan
2014 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2014年の論文
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2014年論文
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2014年論文
@zh-hant
2014年論文
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2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Learning from data: recognizin ...... from visual field measurements
@ast
Learning from data: recognizin ...... from visual field measurements
@en
type
label
Learning from data: recognizin ...... from visual field measurements
@ast
Learning from data: recognizin ...... from visual field measurements
@en
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Learning from data: recognizin ...... from visual field measurements
@ast
Learning from data: recognizin ...... from visual field measurements
@en
P2093
P2860
P1476
Learning from data: recognizin ...... from visual field measurements
@en
P2093
Christopher A Girkin
Christopher Bowd
Felipe A Medeiros
Jeffrey M Liebmann
Madhusudhanan Balasubramanian
Michael H Goldbaum
Siamak Yousefi
P2860
P304
P356
10.1109/TBME.2014.2314714
P577
2014-04-01T00:00:00Z