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Li L 38
Showing records 1 to 25 | Display all abstracts from Li L81972 A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Li L
IEEE Transactions on Medical Imaging 2020; 39: 413-424
82153 The Mechanical Interpretation of Ocular Response Analyzer Parameters
Qin X
BioMed research international 2019; 2019: 5701236
82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Liu H
JAMA ophthalmology 2019; 0:
82522 The Chinese Glaucoma Study Consortium for Patients With Glaucoma: Design, Rationale and Baseline Patient Characteristics
Zhang H
Journal of Glaucoma 2019; 28: 974-978
82153 The Mechanical Interpretation of Ocular Response Analyzer Parameters
Yu M
BioMed research international 2019; 2019: 5701236
81972 A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Xu M
IEEE Transactions on Medical Imaging 2020; 39: 413-424
82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Li L
JAMA ophthalmology 2019; 0:
82522 The Chinese Glaucoma Study Consortium for Patients With Glaucoma: Design, Rationale and Baseline Patient Characteristics
Jia H; Duan X
Journal of Glaucoma 2019; 28: 974-978
81972 A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Liu H
IEEE Transactions on Medical Imaging 2020; 39: 413-424
82153 The Mechanical Interpretation of Ocular Response Analyzer Parameters
Zhang H
BioMed research international 2019; 2019: 5701236
82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Wormstone IM; Qiao C
JAMA ophthalmology 2019; 0:
82153 The Mechanical Interpretation of Ocular Response Analyzer Parameters
Chen X
BioMed research international 2019; 2019: 5701236
82522 The Chinese Glaucoma Study Consortium for Patients With Glaucoma: Design, Rationale and Baseline Patient Characteristics
Li L
Journal of Glaucoma 2019; 28: 974-978
81972 A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Li Y
IEEE Transactions on Medical Imaging 2020; 39: 413-424
82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Zhang C
JAMA ophthalmology 2019; 0:
81972 A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Wang X
IEEE Transactions on Medical Imaging 2020; 39: 413-424
82153 The Mechanical Interpretation of Ocular Response Analyzer Parameters
Li L
BioMed research international 2019; 2019: 5701236
82522 The Chinese Glaucoma Study Consortium for Patients With Glaucoma: Design, Rationale and Baseline Patient Characteristics
Wang H
Journal of Glaucoma 2019; 28: 974-978
81972 A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Jiang L
IEEE Transactions on Medical Imaging 2020; 39: 413-424
82522 The Chinese Glaucoma Study Consortium for Patients With Glaucoma: Design, Rationale and Baseline Patient Characteristics
Wu J
Journal of Glaucoma 2019; 28: 974-978
82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Liu P
JAMA ophthalmology 2019; 0:
81972 A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
Wang Z
IEEE Transactions on Medical Imaging 2020; 39: 413-424
82522 The Chinese Glaucoma Study Consortium for Patients With Glaucoma: Design, Rationale and Baseline Patient Characteristics
Hu J
Journal of Glaucoma 2019; 28: 974-978
82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Li S
JAMA ophthalmology 2019; 0:
82522 The Chinese Glaucoma Study Consortium for Patients With Glaucoma: Design, Rationale and Baseline Patient Characteristics
Cao K
Journal of Glaucoma 2019; 28: 974-978
