Search Results for author:
Found 6 papers, 0 papers with code
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.
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.
Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics
The demand for efficient edge vision has spurred the interest in developing stochastic computing approaches for performing image processing tasks.
Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency
Our research presents a wearable Silent Speech Interface (SSI) technology that excels in device comfort, time-energy efficiency, and speech decoding accuracy for real-world use.
Intelligent machines work in unstructured environments by differential neuromorphic computing
By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.
Human Body Digital Twin: A Master Plan
A human body digital twin (DT) is a virtual representation of an individual's physiological state, created using real-time data from sensors and medical test devices, with the purpose of simulating, predicting, and optimizing health outcomes through advanced analytics and simulations.
