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Found 10 papers, 4 papers with code
RetSTA: An LLM-Based Approach for Standardizing Clinical Fundus Image Reports
The lack of unified standards, including format, terminology, and style, is a great challenge in clinical fundus diagnostic reports, which increases the difficulty for large language models (LLMs) to understand the data.
Wearable intelligent throat enables natural speech in stroke patients with dysarthria
Wearable silent speech systems hold significant potential for restoring communication in patients with speech impairments.
Neuromorphic spatiotemporal optical flow: Enabling ultrafast visual perception beyond human capabilities
Optical flow, inspired by the mechanisms of biological visual systems, calculates spatial motion vectors within visual scenes that are necessary for enabling robotics to excel in complex and dynamic working environments.
ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation Model
The experimental results demonstrate the powerful zero-shot and transfer learning capabilities of ViLReF, verifying the effectiveness of our pre-training strategy.
Deep Learning-Based Longitudinal Prediction of Childhood Myopia Progression Using Fundus Image Sequences and Baseline Refraction Data
In this study, we introduce a novel, high-accuracy method for quantitatively predicting the myopic trajectory and myopia risk in children using only fundus images and baseline refraction data.
RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
The Vision-Language Foundation model is increasingly investigated in the fields of computer vision and natural language processing, yet its exploration in ophthalmology and broader medical applications remains limited.
Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts Via Self-supervised Machine Learning
Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis.
SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results.
VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
To be commensurate with this capacity, in addition to the real data used for pre-training, we also generated and leveraged synthetic ophthalmic imaging data.
Large AI Models in Health Informatics: Applications, Challenges, and the Future
Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions.
