Ophthalmology

Deep learning-based image deconstruction method with maintained saliency.

Visual properties that primarily attract bottom-up attention are collectively referred to as saliency. In this study, to understand the neural activity involved in top-down and bottom-up visual attention, we aim to prepare pairs of natural and unnatural images with common saliency. For this purpose, we propose an image transformation method based on deep neural networks that can generate new image...

Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets.

Screening and diagnosis of diabetic retinopathy disease is a well known problem in the biomedical domain. The use of medical imagery from a patient's eye for detecting the damage caused to blood vessels is a part of the computer-aided diagnosis that has immensely progressed over the past few years due to the advent and success of deep learning. The challenges related to imbalanced datasets, incons...

Automated diagnosing primary open-angle glaucoma from fundus image by simulating human's grading with deep learning.

Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness worldwide. Although deep learning methods have been proposed to diagno...

The development of an eye movement-based deep learning system for laparoscopic surgical skills assessment.

The development of valid, reliable, and objective methods of skills assessment is central to modern surgical training. Numerous rating scales have bee...

Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.

The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central ...

eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing.

Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications ...

Predicting demographic characteristics from anterior segment OCT images with deep learning: A study protocol.

INTRODUCTION: Anterior segment optical coherence tomography (AS-OCT) is a non-contact, rapid, and high-resolution in vivo modality for imaging of the ...

Deep learning for predicting refractive error from multiple photorefraction images.

BACKGROUND: Refractive error detection is a significant factor in preventing the development of myopia. To improve the efficiency and accuracy of refr...

Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning.

PURPOSE: To identify disease activity and effects of intravitreal pegcetacoplan treatment on the topographic progression of geographic atrophy (GA) se...

Massive-Scale Aerial Photo Categorization by Cross-Resolution Visual Perception Enhancement.

Categorizing aerial photographs with varied weather/lighting conditions and sophisticated geomorphic factors is a key module in autonomous navigation,...

Correlation of vascular and fluid-related parameters in neovascular age-related macular degeneration using deep learning.

PURPOSE: To identify correlations between the vascular characteristics of macular neovascularization (MNV) obtained by optical coherence tomography an...

A framework for macroscopic phase-resetting curves for generalised spiking neural networks.

Brain rhythms emerge from synchronization among interconnected spiking neurons. Key properties of such rhythms can be gleaned from the phase-resetting...

A Convolutional Neural-Network-Based Training Model to Estimate Actual Distance of Persons in Continuous Images.

Distance and depth detection plays a crucial role in intelligent robotics. It enables drones to understand their working environment to avoid collisio...

Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete.

Most building structures that are built today are built from concrete, owing to its various favorable properties. Compressive strength is one of the m...

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However...

Robust Detection Model of Vascular Landmarks for Retinal Image Registration: A Two-Stage Convolutional Neural Network.

Registration is useful for image processing in computer vision. It can be applied to retinal images and provide support for ophthalmologists in tracki...

Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.

BACKGROUND: The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogat...

Automated Measurement of Ocular Movements Using Deep Learning-Based Image Analysis.

PURPOSE: Clinical assessment of ocular movements is essential for the diagnosis and management of ocular motility disorders. This study aimed to propo...

Deep learning to distinguish Best vitelliform macular dystrophy (BVMD) from adult-onset vitelliform macular degeneration (AVMD).

Initial stages of Best vitelliform macular dystrophy (BVMD) and adult vitelliform macular dystrophy (AVMD) harbor similar blue autofluorescence (BAF) ...

Estimating heading from optic flow: Comparing deep learning network and human performance.

Convolutional neural networks (CNNs) have made significant advances over the past decade with visual recognition, matching or exceeding human performa...