AIMC Topic: Fundus Oculi
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An advanced deep learning method to detect and classify diabetic retinopathy based on color fundus images.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Aug 7, 2023
BACKGROUND: In this article, we present a computerized system for the analysis and assessment of diabetic retinopathy (DR) based on retinal fundus photographs. DR is a chronic ophthalmic disease and a major reason for blindness in people with diabete...
Learning from small data: Classifying sex from retinal images via deep learning.
PloS one
Aug 3, 2023
Deep learning (DL) techniques have seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for the development of automated diagnostic tools. The facility of its non-invasive acquisition makes reti...
URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.
Experimental biology and medicine (Maywood, N.J.)
Jul 19, 2023
Diabetic retinopathy (DR) will cause blindness if the detection and treatment are not carried out in the early stages. To create an effective treatment strategy, the severity of the disease must first be divided into referral-warranted diabetic retin...
Automated fundus ultrasound image classification based on siamese convolutional neural networks with multi-attention.
BMC medical imaging
Jul 6, 2023
Fundus ultrasound image classification is a critical issue in the medical field. Vitreous opacity (VO) and posterior vitreous detachment (PVD) are two common eye diseases, Now, the diagnosis of these two diseases mainly relies on manual identificatio...
Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis.
Medical image analysis
Jun 30, 2023
Deep neural networks (DNNs) have been widely applied in the medical image community, contributing to automatic ophthalmic screening systems for some common diseases. However, the incidence of fundus diseases patterns exhibits a typical long-tailed di...
SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation.
Computers in biology and medicine
Jun 10, 2023
Retinal vessel segmentation is an important task in medical image analysis and has a variety of applications in the diagnosis and treatment of retinal diseases. In this paper, we propose SegR-Net, a deep learning framework for robust retinal vessel s...
FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading.
Computer methods and programs in biomedicine
May 26, 2023
OBJECTIVE: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up t...
TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF.
Computers in biology and medicine
Apr 17, 2023
Segmentation of retinal fundus images is a crucial part of medical diagnosis. Automatic extraction of blood vessels in low-quality retinal images remains a challenging problem. In this paper, we propose a novel two-stage model combining Transformer U...
An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship.
Scientific reports
Apr 12, 2023
The identification of abnormal findings manifested in retinal fundus images and diagnosis of ophthalmic diseases are essential to the management of potentially vision-threatening eye conditions. Recently, deep learning-based computer-aided diagnosis ...
Performance analysis of pretrained convolutional neural network models for ophthalmological disease classification.
Arquivos brasileiros de oftalmologia
Apr 3, 2023
PURPOSE: This study aimed to evaluate the classification performance of pretrained convolutional neural network models or architectures using fundus image dataset containing eight disease labels.
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