相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images
Jaemin Son et al.
OPHTHALMOLOGY (2020)
CNNs for automatic glaucoma assessment using fundus images: an extensive validation
Andres Diaz-Pinto et al.
BIOMEDICAL ENGINEERING ONLINE (2019)
Quality and and content analysis of fundus images using deep learning
Renoh Johnson Chalakkal et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2019)
Evaluation of deep convolutional neural networks for glaucoma detection
Sang Phan et al.
JAPANESE JOURNAL OF OPHTHALMOLOGY (2019)
Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning
Juan J. Gomez-Valverde et al.
BIOMEDICAL OPTICS EXPRESS (2019)
Faster and transferable deep learning steganalysis on GPU
Ye Dengpan et al.
JOURNAL OF REAL-TIME IMAGE PROCESSING (2019)
From Machine to Machine An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs
Felipe A. Medeiros et al.
OPHTHALMOLOGY (2019)
Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
Stuart Keel et al.
JAMA OPHTHALMOLOGY (2019)
A new image classification method using CNN transfer learning and web data augmentation
Dongmei Han et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs
Zhixi Li et al.
OPHTHALMOLOGY (2018)
Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis
Baidaa Al-Bander et al.
SYMMETRY-BASEL (2018)
Development of a deep residual learning algorithm to screen for glaucoma from fundus photography
Naoto Shibata et al.
SCIENTIFIC REPORTS (2018)
Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification
Mercedes E. Paoletti et al.
REMOTE SENSING (2018)
Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Mark Christopher et al.
SCIENTIFIC REPORTS (2018)
Glaucoma Diagnosis over Eye Fundus Image through Deep Features
Alan Carlos de Moura Lima et al.
2018 25TH INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP) (2018)
Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma
Rashmi Panda et al.
JOURNAL OF MEDICAL IMAGING (2018)
Tracking within Hadronic Showers in the CALICE SDHCAL prototype using a Hough Transform Technique
Z. Deng et al.
JOURNAL OF INSTRUMENTATION (2017)
Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images
Shishir Maheshwari et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2017)
Glaucoma Screening in Nepal: Cup-to-Disc Estimate With Standard Mydriatic Fundus Camera Compared to Portable Nonmydriatic Camera
Sarah E. Miller et al.
AMERICAN JOURNAL OF OPHTHALMOLOGY (2017)
Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images
Cecilia S. Lee et al.
OPHTHALMOLOGY RETINA (2017)
Convolutional Neural Network Transfer for Automated Glaucoma Identification
Jose Ignacio Orlando et al.
12TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS (2017)
Image Super-resolution Based On Self-similarity and Various Patch Size
Luyang Yao et al.
EIGHTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2016) (2016)
The Pathophysiology and Treatment of Glaucoma A Review
Robert N. Weinreb et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2014)
Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040 A Systematic Review and Meta-Analysis
Yih-Chung Tham et al.
OPHTHALMOLOGY (2014)
AUTOMATED GLAUCOMA DETECTION USING HYBRID FEATURE EXTRACTION IN RETINAL FUNDUS IMAGES
M. Muthu Rama Krishnan et al.
JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY (2013)
Transient changes of intraocular pressure and anterior segment configuration after diagnostic mydriasis with 1% tropicamide in children
I-Lun Tsai et al.
CLINICAL AND EXPERIMENTAL OPTOMETRY (2012)
Glaucoma risk index: Automated glaucoma detection from color fundus images
Ruediger Bock et al.
MEDICAL IMAGE ANALYSIS (2010)
Risk Factors for Glaucoma Onset and Progression
Anne L. Coleman et al.
SURVEY OF OPHTHALMOLOGY (2008)
The number of people with glaucoma worldwide in 2010 and 2020
HA Quigley et al.
BRITISH JOURNAL OF OPHTHALMOLOGY (2006)