AIMC Topic: Fundus Oculi
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Combining convolutional neural networks and self-attention for fundus diseases identification.
Scientific reports
Jan 2, 2023
Early detection of lesions is of great significance for treating fundus diseases. Fundus photography is an effective and convenient screening technique by which common fundus diseases can be detected. In this study, we use color fundus images to dist...
Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images.
Scientific reports
Dec 17, 2022
We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss t...
KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD.
Computer methods and programs in biomedicine
Dec 15, 2022
BACKGROUND AND OBJECTIVES: Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal...
Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.
BMC ophthalmology
Dec 12, 2022
BACKGROUND: To verify efficacy of automatic screening and classification of glaucoma with deep learning system.
Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning.
Computer methods and programs in biomedicine
Dec 1, 2022
BACKGROUND AND OBJECTIVES: Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness in developed countries. Although some...
Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods.
Sensors (Basel, Switzerland)
Nov 23, 2022
Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation...
Research on Assistant Diagnosis of Fundus Optic Neuropathy Based on Deep Learning.
Current eye research
Oct 27, 2022
PURPOSE: The purpose of this study was to use the neural network to distinguish optic edema (ODE), and optic atrophy from normal fundus images and try to use visualization to explain the artificial intelligence methods.
Estimation of best corrected visual acuity based on deep neural network.
Scientific reports
Oct 24, 2022
In this study, we investigated a convolutional neural network (CNN)-based framework for the estimation of the best-corrected visual acuity (BCVA) from fundus images. First, we collected 53,318 fundus photographs from the Gyeongsang National Universit...
Do you need sharpened details? Asking MMDC-Net: Multi-layer multi-scale dilated convolution network for retinal vessel segmentation.
Computers in biology and medicine
Oct 13, 2022
Convolutional neural networks (CNN), especially numerous U-shaped models, have achieved great progress in retinal vessel segmentation. However, a great quantity of global information in fundus images has not been fully explored. And the class imbalan...
Peripapillary atrophy classification using CNN deep learning for glaucoma screening.
PloS one
Oct 6, 2022
Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing...
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