AIMC Topic: Retinal Diseases

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Deep learning-enabled ultra-widefield retinal vessel segmentation with an automated quality-optimized angiographic phase selection tool.

Eye (London, England)
OBJECTIVES: To demonstrate the feasibility of a deep learning-based vascular segmentation tool for UWFA and evaluate its ability to automatically identify quality-optimized phase-specific images.

Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.

PloS one
An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively...

Machine learning in optical coherence tomography angiography.

Experimental biology and medicine (Maywood, N.J.)
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
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 ...

"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...