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6.9.5 Other (40)
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Display all abstracts in classification 6.9.5 Other
Search within classification 6.9.5 Other78332 The region of interest localization for glaucoma analysis from retinal fundus image using deep learning
Mitra A
Computer Methods and Programs in Biomedicine 2018; 165: 25-35
78844 An efficient optic cup segmentation method decreasing the influences of blood vessels
Yang C
Biomedical engineering online 2018; 17: 130
78765 Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma
Panda R
Journal of medical imaging (Bellingham, Wash.) 2018; 5: 044003
79160 Automated gonioscopy photography for iridocorneal angle grading
Teixeira F
European Journal of Ophthalmology 2018; 0: 1120672118806436
78779 Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation
Septiarini A
Healthcare informatics research 2018; 24: 335-345
79157 Artificial intelligence and deep learning in ophthalmology
Ting DSW
British Journal of Ophthalmology 2019; 103: 167-175
78813 Fundus image classification methods for the detection of glaucoma: A review
Saba T
Microscopy Research and Technique 2018; 81: 1105-1121
79256 A Fully Automated 3D In-vivo Delineation and Shape Parameterization of the Human Lamina Cribrosa in Optical Coherence Tomography
Syga P
IEEE Transactions on Bio-Medical Engineering 2019; 66: 1422-1428
79160 Automated gonioscopy photography for iridocorneal angle grading
Teixeira F
European Journal of Ophthalmology 2018; 0: 1120672118806436
78813 Fundus image classification methods for the detection of glaucoma: A review
Bokhari STF
Microscopy Research and Technique 2018; 81: 1105-1121
79256 A Fully Automated 3D In-vivo Delineation and Shape Parameterization of the Human Lamina Cribrosa in Optical Coherence Tomography
Sieluzycki C
IEEE Transactions on Bio-Medical Engineering 2019; 66: 1422-1428
79160 Automated gonioscopy photography for iridocorneal angle grading
Sousa DC
European Journal of Ophthalmology 2018; 0: 1120672118806436
78844 An efficient optic cup segmentation method decreasing the influences of blood vessels
Lu M
Biomedical engineering online 2018; 17: 130
78332 The region of interest localization for glaucoma analysis from retinal fundus image using deep learning
Banerjee PS
Computer Methods and Programs in Biomedicine 2018; 165: 25-35
79157 Artificial intelligence and deep learning in ophthalmology
Pasquale LR
British Journal of Ophthalmology 2019; 103: 167-175
78765 Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma
Puhan NB
Journal of medical imaging (Bellingham, Wash.) 2018; 5: 044003
78779 Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation
Harjoko A
Healthcare informatics research 2018; 24: 335-345
78813 Fundus image classification methods for the detection of glaucoma: A review
Sharif M
Microscopy Research and Technique 2018; 81: 1105-1121
78765 Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma
Rao A
Journal of medical imaging (Bellingham, Wash.) 2018; 5: 044003
79157 Artificial intelligence and deep learning in ophthalmology
Peng L
British Journal of Ophthalmology 2019; 103: 167-175
78779 Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation
Pulungan R
Healthcare informatics research 2018; 24: 335-345
79160 Automated gonioscopy photography for iridocorneal angle grading
Leal I
European Journal of Ophthalmology 2018; 0: 1120672118806436
78844 An efficient optic cup segmentation method decreasing the influences of blood vessels
Duan Y
Biomedical engineering online 2018; 17: 130
79256 A Fully Automated 3D In-vivo Delineation and Shape Parameterization of the Human Lamina Cribrosa in Optical Coherence Tomography
Krzyzanowska-Berkowska P
IEEE Transactions on Bio-Medical Engineering 2019; 66: 1422-1428
79157 Artificial intelligence and deep learning in ophthalmology
Campbell JP
British Journal of Ophthalmology 2019; 103: 167-175
