AIMC Topic: Fundus Oculi
Clear Filters Showing 201 to 210 of 491 articles
RFARN: Retinal vessel segmentation based on reverse fusion attention residual network.
PloS one
Dec 3, 2021
Accurate segmentation of retinal vessels is critical to the mechanism, diagnosis, and treatment of many ocular pathologies. Due to the poor contrast and inhomogeneous background of fundus imaging and the complex structure of retinal fundus images, th...
An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture.
Sensors (Basel, Switzerland)
Oct 19, 2021
The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define spec...
Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence.
Scientific reports
Oct 14, 2021
Refined understanding of the association of retinal microstructure with current and future (post-treatment) function in chronic central serous chorioretinopathy (cCSC) may help to identify patients that would benefit most from treatment. In this post...
Deep learning on fundus images detects glaucoma beyond the optic disc.
Scientific reports
Oct 13, 2021
Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. Here, we propose a methodology that advances explainable deep learning in the field of glaucoma dete...
Fundus image segmentation via hierarchical feature learning.
Computers in biology and medicine
Oct 9, 2021
Fundus Image Segmentation (FIS) is an essential procedure for the automated diagnosis of ophthalmic diseases. Recently, deep fully convolutional networks have been widely used for FIS with state-of-the-art performance. The representative deep model i...
Automated detection of severe diabetic retinopathy using deep learning method.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Sep 30, 2021
PURPOSE: The purpose of this study is to develop and validate the intelligent diagnosis of severe DR with lesion recognition based on color fundus photography.
Artificial intelligence-based detection of epimacular membrane from color fundus photographs.
Scientific reports
Sep 29, 2021
Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Al...
Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning.
American journal of ophthalmology
Sep 25, 2021
PURPOSE: To develop deep learning models for identification of sex and age from macular optical coherence tomography (OCT) and to analyze the features for differentiation of sex and age.
Deep learning versus ophthalmologists for screening for glaucoma on fundus examination: A systematic review and meta-analysis.
Clinical & experimental ophthalmology
Sep 22, 2021
BACKGROUND: In this systematic review and meta-analysis, we aimed to compare deep learning versus ophthalmologists in glaucoma diagnosis on fundus examinations.
Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation.
Sensors (Basel, Switzerland)
Sep 15, 2021
Segmentation of retinal vessels is a critical step for the diagnosis of some fundus diseases. To further enhance the performance of vessel segmentation, we propose a method based on a gated skip-connection network with adaptive upsampling (GSAU-Net)...
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