Ophthalmology

Latest AI and machine learning research in ophthalmology for healthcare professionals.

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Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.

BACKGROUND: While expert optometrists tend to rely on a deep understanding of the disease and intuit...

On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.

Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased int...

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.

Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, curr...

Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models.

Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of v...

Dissecting the effectiveness of deep features as metric of perceptual image quality.

There is an open debate on the role of artificial networks to understand the visual brain. Internal ...

Multi scale multi attention network for blood vessel segmentation in fundus images.

Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treat...

Retinal OCT image classification based on MGR-GAN.

Accurately classifying optical coherence tomography (OCT) images is essential for diagnosing and tre...

Optimising deep learning models for ophthalmological disorder classification.

Fundus imaging, a technique for recording retinal structural components and anomalies, is essential ...

Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.

OBJECTIVE: For studies using real-world data, accurately identifying patients with phenotypes of int...

Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision.

INTRODUCTION: Advancements in machine learning (ML) algorithms that make predictions from data witho...

The research progress of biologics in elderly-onset rheumatoid arthritis (EORA).

is a distinct subtype of characterized by heightened treatment challenges due to immune aging and ...

Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.

PURPOSE: Identification of the molecular subtypes in breast cancer allows to optimize treatment stra...

Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.

BACKGROUND: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate th...

DAU-Net: a novel U-Net with dual attention for retinal vessel segmentation.

In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related...

Deep learning generalization for diabetic retinopathy staging from fundus images.

. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making ...

A recurrent YOLOv8-based framework for event-based object detection.

Object detection plays a crucial role in various cutting-edge applications, such as autonomous vehic...

ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus.

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spa...

An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition.

BACKGROUND: Food image recognition, a crucial step in computational gastronomy, has diverse applicat...

Supporting vision-language model few-shot inference with confounder-pruned knowledge prompt.

Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with o...

DCTCNet: Sequency discrete cosine transform convolution network for visual recognition.

The discrete cosine transform (DCT) has been widely used in computer vision tasks due to its ability...

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