AIMC Topic: Retina

Clear Filters Showing 431 to 440 of 482 articles

Hybrid-structure network and network comparative study for deep-learning-based speckle-modulating optical coherence tomography.

Optics express
Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, can resolve sample three-dimensional microstructures. However, speckle noise imposes obvious limitations on OCT resolving capabilities. Here we proposed a deep-learning...

Contrast sensitivity functions in autoencoders.

Journal of vision
Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the co...

SYSTEMATIC CORRELATION OF CENTRAL SUBFIELD THICKNESS WITH RETINAL FLUID VOLUMES QUANTIFIED BY DEEP LEARNING IN THE MAJOR EXUDATIVE MACULAR DISEASES.

Retina (Philadelphia, Pa.)
PURPOSE: To investigate the correlation of volumetric measurements of intraretinal (IRF) and subretinal fluid obtained by deep learning and central retinal subfield thickness (CSFT) based on optical coherence tomography in retinal vein occlusion, dia...

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

Translational vision science & technology
PURPOSE: Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learni...

ANALYSIS OF TRANSFER LEARNING FOR SELECT RETINAL DISEASE CLASSIFICATION.

Retina (Philadelphia, Pa.)
PURPOSE: To analyze the effect of transfer learning for classification of diabetic retinopathy (DR) by fundus photography and select retinal diseases by spectral domain optical coherence tomography (SD-OCT).

EVALUATION OF ARTIFICIAL INTELLIGENCE-BASED QUANTITATIVE ANALYSIS TO IDENTIFY CLINICALLY SIGNIFICANT SEVERE RETINOPATHY OF PREMATURITY.

Retina (Philadelphia, Pa.)
PURPOSE: To evaluate the screening potential of a deep learning algorithm-derived severity score by determining its ability to detect clinically significant severe retinopathy of prematurity (ROP).

Assessing Vision Quality in Retinal Prosthesis Implantees through Deep Learning: Current Progress and Improvements by Optimizing Hardware Design Parameters and Rehabilitation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Retinal prosthesis (RP) is used to partially restore vision in patients with degenerative retinal diseases. Assessing the quality of RP-acquired (i.e., prosthetic) vision is needed to evaluate RP impact and prospects. Spatial distortions caused by el...

Towards Stroke Biomarkers on Fundus Retinal Imaging: A Comparison Between Vasculature Embeddings and General Purpose Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Fundus Retinal imaging is an easy-to-acquire modality typically used for monitoring eye health. Current evidence indicates that the retina, and its vasculature in particular, is associated with other disease processes making it an ideal candidate for...

Generative Image Inpainting for Retinal Images using Generative Adversarial Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The diagnosis and treatment of eye diseases is heavily reliant on the availability of retinal imagining equipment. To increase accessibility, lower-cost ophthalmoscopes, such as the Arclight, have been developed. However, a common drawback of these d...