Samuel Kadoury
Appointment
Solution Network Co-Director
Integrated AI for Health Imaging
Pan-Canadian AI Strategy
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About
Kadoury is a full professor in the Computer and Software Engineering Department at Polytechnique Montréal and researcher at the University of Montréal Research Hospital Center. He is the director of the Medical Image Computing and Analysis Lab and held the Canada Research Chair in Intelligent Image Guided Interventions. His research interests are in medical image computing, machine learning, computer vision and image-guided interventions. He has several international and US patents in the field of image-guided interventions and has led several interdisciplinary projects in precision oncology, combining experts in radiology, biochemistry, immunology, surgery, computer science and machine learning, to develop new predictive biomarkers in liver and lung cancer. He has participated in the deployment of commercial products in the fields of computer vision and biomedical applications, including unsupervised image segmentation using AI-based algorithms and developed radiotherapy solutions integrating AI-based planning and motion compensation, in collaboration with medical physicists and radiation oncologists.
Awards
- IEEE Transactions in Medical Imaging Distinguished Reviewer Award (2022)
- Best paper award Computer Assisted Intervention le cadre de la conférence Medical Imaging Computing and Computer Assisted Interventions (MICCAI) (2020)
- Etoile Effervescence, Montreal In-Vivo. Top 10 researchers under 10 years of experience, demonstrating excellence in biosciences and health engineering (2019)
- Cum Laude Award (Radiological Society of North America) “Recognition of significant technology advances in radiology field” (2016)
- Prix du National Institutes of Health - Merit Award for Prostate Cancer Group (2013)
Relevant Publications
- Romaguera, L. V., Alley, S., Carrier, J. F., & Kadoury, S. (2023). Conditional-based Transformer network with learnable queries for 4D deformation forecasting and tracking. IEEE Transactions on Medical Imaging.
- Le, W. T., Vorontsov, E., Romero, F. P., Seddik, L., Elsharief, M. M., Nguyen-Tan, P. & Kadoury, S. (2022). Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks. Scientific Reports, 12(1), 3183.
- Romaguera, L. V., Plantefève, R., Romero, F. P., Hébert, F., Carrier, J. F., & Kadoury, S. (2020). Prediction of in-plane organ deformation during free-breathing radiotherapy via discriminative spatial transformer networks. Medical image analysis, 64, 101754.
- Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Tang, A., Romero, A., Bengio, Y., Pal, C. & Kadoury, S. (2018). Learning normalized inputs for iterative estimation in medical image segmentation. Medical image analysis, 44, 1-13.
- Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., & Pal, C. (2016). The importance of skip connections in biomedical image segmentation. In International Workshop on Deep Learning in Medical Image Analysis, International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (pp. 179-187). Springer, Cham.