AIMC Topic: Retinal Diseases
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Machine learning in optical coherence tomography angiography.
Experimental biology and medicine (Maywood, N.J.)
Jul 19, 2021
Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular dist...
A Global and Local Enhanced Residual U-Net for Accurate Retinal Vessel Segmentation.
IEEE/ACM transactions on computational biology and bioinformatics
Jun 3, 2021
Retinal vessel segmentation is a critical procedure towards the accurate visualization, diagnosis, early treatment, and surgery planning of ocular diseases. Recent deep learning-based approaches have achieved impressive performance in retinal vessel ...
Artificial intelligence for diagnosis of inherited retinal disease: an exciting opportunity and one step forward.
The British journal of ophthalmology
May 24, 2021
"Keep it simple, scholar": an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging.
International journal of computer assisted radiology and surgery
Apr 30, 2021
PURPOSE: With the recent development of deep learning technologies, various neural networks have been proposed for fundus retinal vessel segmentation. Among them, the U-Net is regarded as one of the most successful architectures. In this work, we sta...
Prediction of causative genes in inherited retinal disorder from fundus photography and autofluorescence imaging using deep learning techniques.
The British journal of ophthalmology
Apr 20, 2021
BACKGROUND/AIMS: To investigate the utility of a data-driven deep learning approach in patients with inherited retinal disorder (IRD) and to predict the causative genes based on fundus photography and fundus autofluorescence (FAF) imaging.
Classification of pachychoroid on optical coherence tomography using deep learning.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Feb 22, 2021
PURPOSE: Pachychoroid is characterized by dilated Haller vessels and choriocapillaris attenuation that are seen on optical coherence tomography (OCT) B-scans. This study investigated the feasibility of using deep learning (DL) models to classify pach...
Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence.
Communications biology
Feb 5, 2021
Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image seg...
Algorithms
Retrospective Studies
Female
Reproducibility of Results
Adult
Animals
Neural Networks, Computer
Artificial Intelligence
Machine Learning
Retina
Humans
Image Interpretation, Computer-Assisted
Clinical Competence
Observer Variation
Retinal Diseases
Male
Multimodal Imaging
Middle Aged
Macaca fascicularis
Tomography, Optical Coherence
Deep Learning
Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.
Medical & biological engineering & computing
Jan 25, 2021
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network...
DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.
Translational vision science & technology
Dec 9, 2020
PURPOSE: To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks.
Machine Learning Techniques for Ophthalmic Data Processing: A Review.
IEEE journal of biomedical and health informatics
Dec 4, 2020
Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approaches proposed for diagnosing ophthalmic diseases during the last four years. Three diseases are addre...
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