Najla Al Turkestani

Orcid: 0000-0002-7650-3638

According to our database1, Najla Al Turkestani authored at least 12 papers between 2021 and 2023.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
Integrative risk predictors of temporomandibular joint osteoarthritis progression.
Proceedings of the Medical Imaging 2023: Image Processing, 2023

Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023 Workshops, 2023

AReg IOS: Automatic Registration on IntraOralScans.
Proceedings of the Shape in Medical Imaging - International Workshop, 2023

Automated Orientation and Registration of Cone-Beam Computed Tomography Scans.
Proceedings of the Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging, 2023

2022
Predicting Osteoarthritis of the Temporomandibular Joint Using Random Forest with Privileged Information.
Proceedings of the Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging, 2022


2021
Feature Selection for Privileged Modalities in Disease Classification.
Proceedings of the Multimodal Learning for Clinical Decision Support, 2021


Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021



TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis.
Proceedings of the Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning, 2021


  Loading...