dr. Sharon Ong

Bio

I am an Assistant Professor in the Department of Cognitive Sciences and Artificial Intelligence, where I am the Principal Investigator for the Deep Learning for Medical Image Data unit. My research focuses on artificial intelligence for image and signal analysis.  In particular, I am interested in developing and deploying AI tools for diagnostic imaging.

Prior to joining Tilburg University, I was a Research Scientist at the Singapore-MIT Alliance for Research and Technology (SMART) Centre in the BioSystems and Micromechanics (BioSyM) interdisciplinary research group. I received my PhD degree in Field Robotics from the University of Sydney, Australia in 2008. In 2003, I received a Bachelor of Engineering (Mechatronics) also from the University of Sydney. 

Expertise

Image Analysis, Object Tracking, Information Fusion, Machine Learning, Stochastic Filtering, Bayesian Estimation

Courses

• RS: Image Analysis
• RS: Spatiotemporal Data Analysis
• Master thesis/Data Science in Action
• Bachelorthesis CSAI

Recent publications

1. Cognitive functioning in untreated glioma patients: the limited predi…

   Boelders, S., Gehring, K., Rutten, G.-J., Ong, S., & Postma, E. (2023). Cognitive functioning in untreated glioma patients: the limited predictive value of clinical variables. Neuro-Oncology, Article noad221.

2. Genetic Classification of Accented Speech from Audio Recordings of Sp…

   Go, G., Roncaglia, P., & Ong, S. (2023). Genetic Classification of Accented Speech from Audio Recordings of Spoken Nonsense Words. Abstract from 35rd Benelux Conference on Artificial Intelligence and the 32th Belgian Dutch Conference on Machine Learning, Delft , Netherlands. https://bnaic2023.tudelft.nl/static/media/BNAICBENELEARN_2023_paper_127.a170261bf03a53c463a6.pdf

3. Musculoskeletal radiologist-level performance by using deep learning …

   Hendrix, N., Hendrix, W., van Dijke, K., Maresch, B., Maas, M., Bollen, S., Scholtens, A., de Jonge, M., Ong, L. L. S., van Ginneken, B., & Rutten, M. (2023). Musculoskeletal radiologist-level performance by using deep learning for detection of scaphoid fractures on conventional multi-view radiographs of hand and wrist. European Radiology, 33(3), 1575-1588.

4. Deep Learning Classifiers to Reduce False Positives in Osteolytic Les…

   Jadikar, M., van Leeuwen, M., van Oudheusden, T., Oei, S., Steunenberg, B., Kint, R., Ranschaert, E., Bosma, G., Saygili, G., & Ong, S. (2023). Deep Learning Classifiers to Reduce False Positives in Osteolytic Lesion Segmentation Results from Low-dose CT Scans of Multiple Myeloma. Paper presented at 35rd Benelux Conference on Artificial Intelligence and the 32th Belgian Dutch Conference on Machine Learning, Delft , Netherlands. https://bnaic2023.tudelft.nl/static/media/BNAICBENELEARN_2023_paper_65.7e5de9cf01a9bf3f4bc8.pdf

5. Predicting Vasovagal Reactions to Needles from Facial Action Units

   Rudokaite, J., Ertugrul, I. O., Ong, S., Janssen, M. P., & Huis in 't Veld, E. (2023). Predicting Vasovagal Reactions to Needles from Facial Action Units. Journal of Clinical Medicine, 12(4), 1-14. Article 1644.