dr. Sharon Ong

Bio

I am an Assistant Professor in the Department of Cognitive Sciences and Artificial Intelligence. My research focuses on artificial intelligence for image and video analysis.  In particular, I am interested in segmentation of biomedical images and tracking of multiple agents to automatically extract insightful information and to create computational models.

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. I received a Bachelor of Engineering (Mechatronics) from the University of Sydney in 2003. 

Expertise

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

Courses

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

Recent publications

1. 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

2. 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.

3. 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

4. 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.

5. A deep learning-based approach to detect and segment osteolytic bone …

   Ong, S., van Leeuwen, M., Saygili, G., Hoff , W., Heres, M., van Oudheusden, T., Steunenberg, B., Kint, R., Bosma, G., & Ranschaert, E. (2022). A deep learning-based approach to detect and segment osteolytic bone lesions in whole-body, low-dose CT imaging of multiple myeloma patients. Poster session presented at EuSoMII Annual Meeting 2022 ‘Your portal to AI’, Valencia, Spain.