Ophthalmology

Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction.

. The image reconstruction of ultrasound computed tomography is computationally expensive with conventional iterative methods. The fully learned direct deep learning reconstruction is promising to speed up image reconstruction significantly. However, for direct reconstruction from measurement data, due to the lack of real labeled data, the neural network is usually trained on a simulation dataset ...

Deep learning-based hemorrhage detection for diabetic retinopathy screening.

Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of the pathological symptoms of diabetic retinopathy that emerges during disease development. Therefore, hemorrhage detection reveals the presence of diabetic retinopathy in the early phase. Diagnosing the disease in its initial stage is crucial to adopt proper treatment so the repercussions can be preve...

Detecting dry eye from ocular surface videos based on deep learning.

OBJECTIVE: To assess the performance of convolutional neural networks (CNNs) for automated diagnosis of dry eye (DE) in patients undergoing video kera...

Early detection of visual impairment in young children using a smartphone-based deep learning system.

Early detection of visual impairment is crucial but is frequently missed in young children, who are capable of only limited cooperation with standard ...

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existi...

Real-time fundus reconstruction and intraocular mapping using an ophthalmic endoscope.

BACKGROUND: Robotic ophthalmic endoscope holders allow surgeons to execute dual-hand operations in eye surgery. To prevent needle-like endoscopes from...

Deeper neural network models better reflect how humans cope with contrast variation in object recognition.

Visual inputs are far from ideal in everyday situations such as in the fog where the contrasts of input stimuli are low. However, human perception rem...

A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach.

Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models ...

Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network.

We proposed a novel deep convolutional neural network (DCNN) using inverted residuals and linear bottleneck layers for diagnosing grey blight disease ...

Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis.

PURPOSE: To compare the performance of 2 relatively recent geometric deep learning techniques in diagnosing glaucoma from a single optical coherence t...

Robotics and cybersurgery in ophthalmology: a current perspective.

Ophthalmology is one of the most enriched fields, allowing the domain of artificial intelligence to be part of its point of interest in scientific res...

Applications of Artificial Intelligence and Deep Learning in Glaucoma.

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standard...

Decoding of human identity by computer vision and neuronal vision.

Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer visi...

Deep Learning with Attention Mechanisms for Road Weather Detection.

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can...

Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to t...

Early experience with low-pass filtered images facilitates visual category learning in a neural network model.

Humans are born with very low contrast sensitivity, meaning that inputs to the infant visual system are both blurry and low contrast. Is this solely a...

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes.

Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in...

Vision-Based Efficient Robotic Manipulation with a Dual-Streaming Compact Convolutional Transformer.

Learning from visual observation for efficient robotic manipulation is a hitherto significant challenge in Reinforcement Learning (RL). Although the c...

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach.

Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed grad...

Towards Haptic-Based Dual-Arm Manipulation.

Vision is the main component of current robotics systems that is used for manipulating objects. However, solely relying on vision for hand-object pose...