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WGW-2021

6.8.2 Posterior segment (36)

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86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Barros DMS
Biomedical engineering online 2020; 19: 20
86466 Data on OCT and fundus images for the detection of glaucoma
Raja H
Data in brief 2020; 29: 105342
86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Lee J
Scientific reports 2020; 10: 3280
86662 Defective angles of localized retinal nerve fiber layer reflect the severity of visual field defect- a cross-sectional analysis
Chen A
BMC Ophthalmology 2020; 20: 141
86692 Characteristics of Normal-tension Glaucoma Patients with Temporal Retinal Nerve Fibre Defects
Yum HR
Scientific reports 2020; 10: 6362
86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Yang HK
American Journal of Ophthalmology 2020; 216: 140-146
86662 Defective angles of localized retinal nerve fiber layer reflect the severity of visual field defect- a cross-sectional analysis
Lai IC
BMC Ophthalmology 2020; 20: 141
86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Kim YK
Scientific reports 2020; 10: 3280
86466 Data on OCT and fundus images for the detection of glaucoma
Akram MU
Data in brief 2020; 29: 105342
86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Moura JCC
Biomedical engineering online 2020; 19: 20
86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Kim YJ
American Journal of Ophthalmology 2020; 216: 140-146
86692 Characteristics of Normal-tension Glaucoma Patients with Temporal Retinal Nerve Fibre Defects
Park HL
Scientific reports 2020; 10: 6362
86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Freire CR
Biomedical engineering online 2020; 19: 20
86692 Characteristics of Normal-tension Glaucoma Patients with Temporal Retinal Nerve Fibre Defects
Park CK
Scientific reports 2020; 10: 6362
86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Sung JY
American Journal of Ophthalmology 2020; 216: 140-146
86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Ha A
Scientific reports 2020; 10: 3280
86466 Data on OCT and fundus images for the detection of glaucoma
Khawaja SG
Data in brief 2020; 29: 105342
86662 Defective angles of localized retinal nerve fiber layer reflect the severity of visual field defect- a cross-sectional analysis
Cho WH
BMC Ophthalmology 2020; 20: 141
86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Taleb AC
Biomedical engineering online 2020; 19: 20
86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Kim DH
American Journal of Ophthalmology 2020; 216: 140-146
86662 Defective angles of localized retinal nerve fiber layer reflect the severity of visual field defect- a cross-sectional analysis
Lai HY
BMC Ophthalmology 2020; 20: 141
86466 Data on OCT and fundus images for the detection of glaucoma
Arslan M
Data in brief 2020; 29: 105342
86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Sun S
Scientific reports 2020; 10: 3280
86466 Data on OCT and fundus images for the detection of glaucoma
Ramzan A
Data in brief 2020; 29: 105342
86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Kim KG
American Journal of Ophthalmology 2020; 216: 140-146

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WGW-2021