I'm a M.Sc. student at McGill supervised by Professor Tal Arbel at the Probabilistic Vision Group (PVG). For my M.Sc. thesis, I'm focusing on adapting deep learning methods to biases across different Multiple Sclerosis imaging datasets. Previously, during my B.Eng at Ryerson University, I held two research assistant positions. During those positions, I worked with a wide variety of medical computer vision problems including Augmented Reality for surgical navigation, Optical Coherence Tomography for retinal and skin imaging, and deep learning methods for maternal fetal MRI.
You can find my complete CV here.
B.Eng
Ryerson University, Toronto, Canada
CGPA: 3.98/4.0 (4.24/4.33)
M.Sc.
McGill University, Montreal, Canada
CGPA: 4.0/4.0
Research Assistant
Ryerson University and St. Michael's Hosptial, Canada
Research Assistant
Ryerson University and Sunnybrook Health Sciences Centre, Canada
B. Nichyporuk, J. Cardinell, J. Szeto, R. Mehta, D.L. Arnold, S.Tsaftaris and T. Arbel, "Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation", in Proceedings of the MICCAI 2021 Workshop: 3rd MICCAI Workshop on Domain Adaptation and Representation Transfer (DART), held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), held virtually (Strasbourg, France), September 2021. Won Best Paper Award of the DART workshop
B. Nichyporuk, K. Vasilevski, A. Hu, C. Myers-Colet, J. Cardinell, J. Szeto, J.P. Falet, E. Zimmermann, J. Schroeter, D. L. Arnold, and T. Arbel. “Consensus Learning with Multi-Rater Labels for Segmenting and Detecting New Lesions,” MSSEG-2 challenge proceedings (2021): Multiple sclerosis new lesions segmentation challenge using a data management and processing infrastructure.
2021, made with in pure Bootstrap 4, inspired by Academic Template for Hugo