AIMC Topic: Retinal Diseases
Clear Filters Showing 81 to 90 of 157 articles
How to Extract More Information With Less Burden: Fundus Image Classification and Retinal Disease Localization With Ophthalmologist Intervention.
IEEE journal of biomedical and health informatics
Dec 4, 2020
Image classification using convolutional neural networks (CNNs) outperforms other state-of-the-art methods. Moreover, attention can be visualized as a heatmap to improve the explainability of results of a CNN. We designed a framework that can generat...
Deep Learning-Based Detection of Endothelial Tip Cells in the Oxygen-Induced Retinopathy Model.
Toxicologic pathology
Dec 2, 2020
Proliferative retinopathies, such as diabetic retinopathy and retinopathy of prematurity, are leading causes of vision impairment. A common feature is a loss of retinal capillary vessels resulting in hypoxia and neuronal damage. The oxygen-induced re...
Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis.
IEEE transactions on medical imaging
Nov 30, 2020
The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging due to the requirement of a large amount of human-annotated data. Rec...
Deep Learning for Automated Sorting of Retinal Photographs.
Ophthalmology. Retina
Mar 17, 2020
PURPOSE: Though the domain of big data and artificial intelligence in health care continues to evolve, there is a lack of systemic methods to improve data quality and streamline the preparation process. To address this, we aimed to develop an automat...
Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population.
PloS one
Mar 5, 2020
Hypertension is the leading risk factor of cardiovascular disease and has profound effects on both the structure and function of the microvasculature. Abnormalities of the retinal vasculature may reflect the degree of microvascular damage due to hype...
Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review.
Translational vision science & technology
Feb 18, 2020
Artificial intelligence (AI)-based automated classification and segmentation of optical coherence tomography (OCT) features have become increasingly popular. However, its 3-dimensional volumetric nature has made developing an algorithm that generaliz...
Domain-invariant interpretable fundus image quality assessment.
Medical image analysis
Jan 30, 2020
Objective and quantitative assessment of fundus image quality is essential for the diagnosis of retinal diseases. The major factors in fundus image quality assessment are image artifact, clarity, and field definition. Unfortunately, most of existing ...
Development and Evaluation of a Deep Learning System for Screening Retinal Hemorrhage Based on Ultra-Widefield Fundus Images.
Translational vision science & technology
Jan 29, 2020
PURPOSE: To develop and evaluate a deep learning (DL) system for retinal hemorrhage (RH) screening using ultra-widefield fundus (UWF) images.
AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.
Medical & biological engineering & computing
Nov 14, 2019
Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (A...
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 ...
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