AIMC Topic: Fundus Oculi
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Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.
Artificial intelligence in medicine
Jul 26, 2019
Diabetic retinopathy (DR) is an eye disease that victimize the people suffering from diabetes from many years. The severe form of DR results in form of the blindness that can initially be controlled by the DR-screening oriented treatment. The effecti...
Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning.
Acta ophthalmologica
Jul 25, 2019
PURPOSE: To assess the use of deep learning (DL) for computer-assisted glaucoma identification, and the impact of training using images selected by an active learning strategy, which minimizes labelling cost. Additionally, this study focuses on the e...
Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs.
Clinical & experimental ophthalmology
Jul 25, 2019
IMPORTANCE: Detection of early onset neovascular age-related macular degeneration (AMD) is critical to protecting vision.
Visual Acuity
Predictive Value of Tests
Area Under Curve
Diagnosis, Computer-Assisted
Deep Learning
ROC Curve
Retrospective Studies
Wet Macular Degeneration
Female
Fundus Oculi
Photography
Ophthalmologists
Humans
Reproducibility of Results
False Positive Reactions
Sensitivity and Specificity
Choroidal Neovascularization
Algorithms
Fluorescein Angiography
Male
Middle Aged
Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading.
Scientific reports
Jul 24, 2019
Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening and diagnosis. This la...
Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning.
BMC medical informatics and decision making
Jul 17, 2019
BACKGROUND: With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods continue to provide reliable and s...
Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.
Artificial intelligence in medicine
Jul 10, 2019
Diabetic retinopathy (DR) is the most common cause of blindness in middle-age subjects and low DR screening rates demonstrates the need for an automated image assessment system, which can benefit from the development of deep learning techniques. Ther...
Multi-proportion channel ensemble model for retinal vessel segmentation.
Computers in biology and medicine
Jul 9, 2019
OBJECTIVE: A novel supervised method that is based on the Multi-Proportion Channel Ensemble Model (MPC-EM) is proposed to obtain more vessel details with reduced computational complexity.
Scale-space approximated convolutional neural networks for retinal vessel segmentation.
Computer methods and programs in biomedicine
Jun 29, 2019
BACKGROUND AND OBJECTIVE: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation...
Artery-vein segmentation in fundus images using a fully convolutional network.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 15, 2019
Epidemiological studies demonstrate that dimensions of retinal vessels change with ocular diseases, coronary heart disease and stroke. Different metrics have been described to quantify these changes in fundus images, with arteriolar and venular calib...
Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.
PloS one
Jun 6, 2019
We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-impl...
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