AIMC Topic: Fundus Oculi

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SegR-Net: A deep learning framework with multi-scale feature fusion for robust retinal vessel segmentation.

Computers in biology and medicine
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
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
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
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
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.

A deep learning-based framework for retinal fundus image enhancement.

PloS one
PROBLEM: Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis.

Sex determination using color fundus parameters in older adults of Kumejima population study.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: Deep learning artificial intelligence can determine the sex using only fundus photographs. However, the factors used by deep learning to determine the sex are not visible. Therefore, the purpose of the study was to determine whether the sex ...

Application of Artificial Intelligence to Quantitative Assessment of Fundus Tessellated Density in Young Adults with Different Refractions.

Ophthalmic research
INTRODUCTION: The aim of this study was to quantitatively assess fundus tessellated density (FTD) and associated factors by artificial intelligence (AI) in young adults.

Automatic Detection of Peripheral Retinal Lesions From Ultrawide-Field Fundus Images Using Deep Learning.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PURPOSE: To establish a multilabel-based deep learning (DL) algorithm for automatic detection and categorization of clinically significant peripheral retinal lesions using ultrawide-field fundus images.

Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search.

Computers in biology and medicine
Analysis of the vascular tree is the basic premise to automatically diagnose retinal biomarkers associated with ophthalmic and systemic diseases, among which accurate identification of intersection and bifurcation points is quite challenging but impo...