Ke Ma

Orcid: 0000-0001-8853-7782

Affiliations:
  • Sun Yat-sen University, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangzhou, China
  • South China University of Technology, Guangzhou, School of Mechanical and Automotive Engineering, Shien-Ming Wu School of Intelligent Engineering, China


According to our database1, Ke Ma authored at least 10 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
Automatic vessel crossing and bifurcation detection based on multi-attention network vessel segmentation and directed graph search.
Comput. Biol. Medicine, March, 2023

Inter-subject prediction of pediatric emergence delirium using feature selection and classification from spontaneous EEG signals.
Biomed. Signal Process. Control., 2023

2022
Efficient Subject-Independent Detection of Anterior Cruciate Ligament Deficiency Based on Marine Predator Algorithm and Support Vector Machine.
IEEE J. Biomed. Health Informatics, 2022

OMSN and FAROS: OCTA Microstructure Segmentation Network and Fully Annotated Retinal OCTA Segmentation Dataset.
CoRR, 2022

2021
A spasticity assessment method for voluntary movement using data fusion and machine learning.
Biomed. Signal Process. Control., 2021

Trunk compensation electromyography features purification and classification model using generative adversarial network.
Biomed. Signal Process. Control., 2021

2020
Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy.
IEEE J. Biomed. Health Informatics, 2020

A novel feature separation model exchange-GAN for facial expression recognition.
Knowl. Based Syst., 2020

2019
sEMG-Based Detection of Compensation Caused by Fatigue During Rehabilitation Therapy: A Pilot Study.
IEEE Access, 2019

A Continuous Estimation Model of Upper Limb Joint Angles by Using Surface Electromyography and Deep Learning Method.
IEEE Access, 2019


  Loading...