Latest AI and machine learning research in ophthalmology for healthcare professionals.
In low-light environments, the performance of computer vision algorithms often deteriorates significantly, adversely affecting key vision tasks such as segmentation, detection, and classification. With the rapid advancement of deep learning, its application to low-light image processing has attracted widespread attention and seen significant progress in recent years. However, there remains a lac...
: This study introduces a novel method for the automated detection and quantification of meibomian gland morphology using gray value distribution profiles. The approach addresses limitations in traditional manual and deep learning-based meibography analysis, which are often time-consuming and prone to variability. : This study enrolled 100 volunteers (mean age 40 ± 16 years, range 18-85) who suffe...
: In 2010, approximately 60.5 million people were affected by glaucoma, making it the leading cause of permanent vision impairment globally. With the ...
Adaptive optics scanning light ophthalmoscopy (AOSLO) reveals individual retinal cells and their function, microvasculature, and micropathologies in v...
Achieving deep alignment between vision and language remains a central challenge for Multimodal Large Language Models (MLLMs). These models often fa...
We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QM...
Estimating a child's age from ocular biometric images is challenging due to subtle physiological changes and the limited availability of longitudina...
In this study, an autonomous visual-guided robotic cotton-picking system, built on a Clearpath's Husky robot platform and the Cotton-Eye perception ...
Self-supervised learning (SSL) has enabled Vision Transformers (ViTs) to learn robust representations from large-scale natural image datasets, enhan...
Vision-Language Models (VLMs) learn joint representations by mapping images and text into a shared latent space. However, recent research highlights...
Enabling intelligent agents to comprehend and interact with 3D environments through natural language is crucial for advancing robotics and human-com...
Learning bimanual manipulation is challenging due to its high dimensionality and tight coordination required between two arms. Eye-in-hand imitation...
Glaucoma is a leading cause of irreversible blindness, with challenges persisting in early diagnosis, disease progression, and surgical outcome predic...
Radiologists' eye movements during medical image interpretation reflect their perceptual-cognitive processes of diagnostic decisions. The eye movement...
This dissertation considers Open-world Robot Manipulation, a manipulation problem where a robot must generalize or quickly adapt to new objects, sce...
Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language under...
Dense visual prediction tasks have been constrained by their reliance on predefined categories, limiting their applicability in real-world scenarios...
The Hillel Yaffe Age Related Macular Degeneration (HYAMD) dataset is a longitudinal collection of 1,560 Digital Fundus Images (DFIs) from 325 patien...
Vision GNN (ViG) demonstrates superior performance by representing images as graph structures, providing a more natural way to capture irregular sem...
The unique vascularized anatomy of the human eye, encased in the retina, provides an opportunity to act as a window for human health. The retinal st...