AIMC Topic: Retinal Diseases
Clear Filters Showing 91 to 100 of 157 articles
Convolutional Neural Networks for Spectroscopic Analysis in Retinal Oximetry.
Scientific reports
Aug 6, 2019
Retinal oximetry is a non-invasive technique to investigate the hemodynamics, vasculature and health of the eye. Current techniques for retinal oximetry have been plagued by quantitatively inconsistent measurements and this has greatly limited their ...
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...
Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.
Computer methods and programs in biomedicine
Jun 14, 2019
BACKGROUND AND OBJECTIVES: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance ...
Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.
Ophthalmology
May 31, 2019
PURPOSE: To develop and evaluate deep learning models that screen multiple abnormal findings in retinal fundus images.
Generating retinal flow maps from structural optical coherence tomography with artificial intelligence.
Scientific reports
Apr 5, 2019
Despite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that...
A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.
IEEE transactions on medical imaging
Mar 20, 2019
Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a...
Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.
IEEE transactions on medical imaging
Feb 8, 2019
Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases accordi...
Fully automated detection of retinal disorders by image-based deep learning.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Jan 4, 2019
PURPOSE: With the aging population and the global diabetes epidemic, the prevalence of age-related macular degeneration (AMD) and diabetic macular edema (DME) diseases which are the leading causes of blindness is further increasing. Intravitreal inje...
Human/robotic interaction: vision limits performance in simulated vitreoretinal surgery.
Acta ophthalmologica
Dec 27, 2018
PURPOSE: Compare accuracy and precision in XYZ of stationary and dynamic tasks performed by surgeons with and without the use of a tele-operated robotic micromanipulator in a simulated vitreoretinal environment. The tasks were performed using a surgi...
Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage.
Bioscience reports
Dec 7, 2018
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In ...
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