Latest AI and machine learning research in ophthalmology for healthcare professionals.
Automated retinal vessel segmentation is among the most significant application and research topics in ophthalmologic image analysis. Deep learning based retinal vessel segmentation models have attracted much attention in the recent years. However, current deep network designs tend to predominantly focus on vessels which are easy to segment, while overlooking vessels which are more difficult to se...
Retinal pigment epithelial (RPE) cells play an important role in nourishing retinal neurosensory photoreceptor cells, and numerous blinding diseases are associated with RPE defects. Their fluorescence signature can now be visualized in the living human eye using adaptive optics (AO) imaging combined with indocyanine green (ICG), which motivates us to develop an automated RPE detection method to im...
Image classification using convolutional neural networks (CNNs) outperforms other state-of-the-art methods. Moreover, attention can be visualized as a...
Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approac...
Deep learning methods for diabetic retinopathy (DR) diagnosis are usually criticized as being lack of interpretability in the diagnostic result, thus ...
The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomogra...
PURPOSE: To evaluate the performance of a deep learning algorithm in the detection of referral-warranted diabetic retinopathy (RDR) on low-resolution ...
Building an efficient and reliable collision perception visual system is a challenging problem for future robots and autonomous vehicles. The biologic...
Biological visual systems exhibit abundant recurrent connectivity. State-of-the-art neural network models for visual recognition, by contrast, rely he...
Proliferative retinopathies, such as diabetic retinopathy and retinopathy of prematurity, are leading causes of vision impairment. A common feature is...
PURPOSE: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis tha...
An Electroencephalogram (EEG) is often tarnished by various categories of artifacts. Numerous efforts have been taken to improve its quality by elimin...
PURPOSE: Compounded eye drop solutions of vancomycin hydrochloride have important clinical applications, such as postoperative antimicrobial prophylax...
Glaucoma, the group of eye diseases is characterized by increased intraocular pressure, optic neuropathy and visual field defect patterns. Early and c...
In this article, two novel deep learning methods are proposed for displacement estimation in ultrasound elastography (USE). Although convolutional neu...
AIM: To develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type...
BACKGROUND AND PURPOSE: In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screen...
BACKGROUND: Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decisio...
Medical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of "deep learn...
Efficient electrogeneration of hydrogen peroxide (HO) is critical for treatment of refractory pollutants by the electro-Fenton process. An effective s...