Retinal Hemorrhage

Showing 11 to 14 of 14 articles
Clear Filters

Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.

BMC ophthalmology Nov 6, 2018
BACKGROUND: Convolution neural networks have been considered for automatic analysis of fundus images to detect signs of diabetic retinopathy but suffer from low sensitivity.
Fundus Oculi Image Interpretation, Computer-Assisted Sensitivity and Specificity Retinal Hemorrhage Microaneurysm Exudates and Transudates Diagnostic Techniques, Ophthalmological Humans Diabetic Retinopathy Neural Networks, Computer
View on PubMed DOI

Pneumatic Displacement of Submacular Hemorrhage with Subretinal Air and Tissue Plasminogen Activator: Initial United States Experience.

Ophthalmology. Retina Sep 28, 2017
PURPOSE: To present the initial multicenter experience of using subretinal air injection in combination with tissue plasminogen activator (tPA) at the time of pars plana vitrectomy (PPV) to displace submacular hemorrhage (SMH).
Air Endotamponade Fluorescein Angiography Injections, Intraocular Retina Tissue Plasminogen Activator Fundus Oculi United States Treatment Outcome Aged Aged, 80 and over Time Factors Vitrectomy Male Retrospective Studies Retinal Hemorrhage Fibrinolytic Agents Middle Aged Female Tomography, Optical Coherence Humans Follow-Up Studies Visual Acuity
View on PubMed DOI

Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images.

IEEE transactions on medical imaging Feb 8, 2016
Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is...
Neural Networks, Computer Machine Learning Humans Retinal Hemorrhage Fundus Oculi Image Interpretation, Computer-Assisted Databases, Factual
View on PubMed DOI

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

BMC ophthalmology Jan 27, 2015
BACKGROUND: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD).
Retinal Pigment Epithelium Retinal Drusen Macula Lutea Subretinal Fluid Decision Trees Area Under Curve Macular Degeneration Retinal Hemorrhage ROC Curve Image Processing, Computer-Assisted Male Machine Learning Female Humans Aged Sensitivity and Specificity
View on PubMed DOI
Popular Topics
Recent Journals