Ophthalmology

Tensor-Based Emotional Category Classification via Visual Attention-Based Heterogeneous CNN Feature Fusion.

The paper proposes a method of visual attention-based emotion classification through eye gaze analysis. Concretely, tensor-based emotional category classification via visual attention-based heterogeneous convolutional neural network (CNN) feature fusion is proposed. Based on the relationship between human emotions and changes in visual attention with time, the proposed method performs new gaze-bas...

Towards implementation of AI in New Zealand national diabetic screening program: Cloud-based, robust, and bespoke.

Convolutional Neural Networks (CNNs) have become a prominent method of AI implementation in medical classification tasks. Grading Diabetic Retinopathy (DR) has been at the forefront of the development of AI for ophthalmology. However, major obstacles remain in the generalization of these CNNs onto real-world DR screening programs. We believe these difficulties are due to use of 1) small training d...

Multiple-target tracking in human and machine vision.

Humans are able to track multiple objects at any given time in their daily activities-for example, we can drive a car while monitoring obstacles, pede...

Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study.

Wrong-site surgeries can occur due to the absence of an appropriate surgical time-out. However, during a time-out, surgical participants are unable to...

Role of Deep Learning-Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression.

PURPOSE: To quantitatively measure hyperreflective foci (HRF) during the progression of geographic atrophy (GA) secondary to age-related macular degen...

Artificial Intelligence Mapping of Structure to Function in Glaucoma.

PURPOSE: To develop an artificial intelligence (AI)-based structure-function (SF) map relating retinal nerve fiber layer (RNFL) damage on spectral dom...

Learning visual features under motion invariance.

Humans are continuously exposed to a stream of visual data with a natural temporal structure. However, most successful computer vision algorithms work...

Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks.

Optic disc (OD) and optic cup (OC) segmentation are important steps for automatic screening and diagnosing of optic nerve head abnormalities such as g...

Automatic optic nerve head localization and cup-to-disc ratio detection using state-of-the-art deep-learning architectures.

Computer vision has greatly advanced recently. Since AlexNet was first introduced, many modified deep learning architectures have been developed and t...

Diabetic retinopathy and ultrawide field imaging.

The introduction of ultrawide field imaging has allowed the visualization of approximately 82% of the total retinal area compared to only 30% using 7-...

Visual information flow in Wilson-Cowan networks.

In this paper, we study the communication efficiency of a psychophysically tuned cascade of Wilson-Cowan and divisive normalization layers that simula...

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography.

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of c...

Portable deep learning singlet microscope.

Having the least lenses, the significant feature of the singlet imaging system, helps the development of the portable and cost-effective microscopes. ...

Advanced robotic surgical systems in ophthalmology.

In this paper, an overview of advanced robotic surgical systems in ophthalmology is provided. The systems are introduced as representative examples of...

NFN＋: A novel network followed network for retinal vessel segmentation.

In the early diagnosis of diabetic retinopathy, the morphological attributes of blood vessels play an essential role to construct a retinal computer-a...

Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.

Patient perceptions of wait time during outpatient office visits can affect patient satisfaction. Providing accurate information about wait times coul...

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in partic...

CATCH: Characterizing and Tracking Colloids Holographically Using Deep Neural Networks.

In-line holographic microscopy provides an unparalleled wealth of information about the properties of colloidal dispersions. Analyzing one colloidal p...

Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model.

We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness...

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans.

PURPOSE: The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes t...