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WGC-2021

6.9.5 Other (290)

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82450 The impact of artificial intelligence in the diagnosis and management of glaucoma
Mayro EL
Eye 2020; 34: 1-11
82206 Ophthalmic Research Lecture 2018: DARC as a Potential Surrogate Marker
Yap TE
Ophthalmic Research 2020; 63: 1-7
82108 A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
Raghavendra U
Journal of Medical Systems 2019; 43: 299
82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Chiquet C
Acta Ophthalmologica 2020; 98: e88-e93
82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Phene S
Ophthalmology 2019; 126: 1627-1639
82099 Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis
Zhou W
Medical and Biological Engineering and Computing 2019; 57: 2055-2067
82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Shang Q
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749
82875 Glaucoma Assessment from OCT images using Capsule Network
Gaddipati DJ
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 5581-5584
82453 Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images
Mvoulana A
Computerized Medical Imaging and Graphics 2019; 77: 101643
82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Mao Z
Biomedical optics express 2019; 10: 5832-5851
82088 Network-based features for retinal fundus vessel structure analysis
Amil P
PLoS ONE 2019; 14: e0220132
82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Liu H
JAMA ophthalmology 2019; 0:
82092 Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning
Hemelings R
Acta Ophthalmologica 2020; 98: e94-e100
82872 A New Texture-Based Segmentation Method for Optical Coherence Tomography Images
Monemian M
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4750-4753
81601 Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Wang S
IEEE Transactions on Medical Imaging 2019; 38: 2485-2495
82867 A novel method for retinal vessel segmentation and diameter measurement using high speed video
Rezaeian M
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2781-2784
82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Phasuk S
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907
82796 Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning
Zhao R
Medical Image Analysis 2020; 60: 101593
82865 Conditional Adversarial Transfer for Glaucoma Diagnosis
Wang J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2032-2035
82744 Glaucoma detection using image processing techniques: A literature review
Sarhan A
Computerized Medical Imaging and Graphics 2019; 78: 101657
82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Orlando JI
Medical Image Analysis 2020; 59: 101570
82333 Automated anterior chamber angle pigmentation analyses using 360° gonioscopy
Matsuo M
British Journal of Ophthalmology 2019; 0:
82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Liu S
Computers in Biology and Medicine 2019; 115: 103485
82612 Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples
Xu YL
Sensors (Basel, Switzerland) 2019; 19:
81946 Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Rogers TW
Eye 2019; 33: 1791-1797

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