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Retinal Diseases

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"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
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
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
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...

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Medical & biological engineering & computing
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
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
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...

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
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
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...