AIMC Topic: Fundus Oculi

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Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.

Artificial intelligence in medicine Jul 10, 2019
Diabetic retinopathy (DR) is the most common cause of blindness in middle-age subjects and low DR screening rates demonstrates the need for an automated image assessment system, which can benefit from the development of deep learning techniques. Ther...
Humans Diabetic Retinopathy Deep Learning ROC Curve Fundus Oculi Neural Networks, Computer Sensitivity and Specificity Diagnosis, Computer-Assisted
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Multi-proportion channel ensemble model for retinal vessel segmentation.

Computers in biology and medicine Jul 9, 2019
OBJECTIVE: A novel supervised method that is based on the Multi-Proportion Channel Ensemble Model (MPC-EM) is proposed to obtain more vessel details with reduced computational complexity.
Deep Learning Fundus Oculi Image Interpretation, Computer-Assisted Retinal Vessels Humans Algorithms Databases, Factual
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Scale-space approximated convolutional neural networks for retinal vessel segmentation.

Computer methods and programs in biomedicine Jun 29, 2019
BACKGROUND AND OBJECTIVE: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation...
Algorithms Deep Learning ROC Curve Humans Neural Networks, Computer Fundus Oculi False Positive Reactions Retinal Diseases Normal Distribution Image Processing, Computer-Assisted Area Under Curve Signal Processing, Computer-Assisted Retinal Vessels
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Artery-vein segmentation in fundus images using a fully convolutional network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Jun 15, 2019
Epidemiological studies demonstrate that dimensions of retinal vessels change with ocular diseases, coronary heart disease and stroke. Different metrics have been described to quantify these changes in fundus images, with arteriolar and venular calib...
Photography Retinal Vessels Retinal Artery Retinal Vein Benchmarking Fundus Oculi Datasets as Topic Humans Deep Learning
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Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

PloS one Jun 6, 2019
We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-impl...
India Image Processing, Computer-Assisted Fundus Oculi Fluorescein Angiography Female Male Deep Learning Humans Diabetic Retinopathy Databases, Factual
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Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images.

Ophthalmology May 31, 2019
PURPOSE: To develop and evaluate deep learning models that screen multiple abnormal findings in retinal fundus images.
Algorithms Female Aged Aged, 80 and over Adult Sensitivity and Specificity Humans Neural Networks, Computer Machine Learning Fundus Oculi Image Interpretation, Computer-Assisted Datasets as Topic Retinal Diseases Deep Learning ROC Curve Area Under Curve Cross-Sectional Studies Male Middle Aged
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Microaneurysm detection in fundus images using a two-step convolutional neural network.

Biomedical engineering online May 29, 2019
BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since micro...
Fundus Oculi Microaneurysm Deep Learning Image Processing, Computer-Assisted Tomography, X-Ray Computed
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Automated OCT angiography image quality assessment using a deep learning algorithm.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie May 22, 2019
PURPOSE: To expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA).
Reproducibility of Results Deep Learning Tomography, Optical Coherence Fundus Oculi Artifacts Fluorescein Angiography Neural Networks, Computer Algorithms Retrospective Studies Humans Retinal Vessels
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BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation.

International journal of medical informatics Apr 1, 2019
BACKGROUND AND OBJECTIVE: The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of simi...
Retinal Vessels Algorithms Fundus Oculi Supervised Machine Learning Humans Neural Networks, Computer
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Validation of a Deep Learning Model to Screen for Glaucoma Using Images from Different Fundus Cameras and Data Augmentation.

Ophthalmology. Glaucoma Apr 1, 2019
PURPOSE: To validate a deep residual learning algorithm to diagnose glaucoma from fundus photography using different fundus cameras at different institutes.
Cross-Sectional Studies Male Deep Learning ROC Curve Algorithms Female Aged Humans Glaucoma Fundus Oculi Optic Disk Fluorescein Angiography
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