Probabilistic Vision Group (PVG)

The Probabilistic Vision Group (PVG) and Medical Imaging Labs are led by Prof. Tal Arbel, CIFAR AI Chair, Mila, and are located within the centre for Intelligent machines (CIM), Department of Electrical & Computer Engineering, McGill University. The research group lies at the juxtaposition of the fields of computer vision, machine learning and medical image analysis. Established in 2001, the PVG is an internationally-recognized, interdisciplinary research lab focused on developing probabilistic machine learning frameworks in computer vision developed for a wide range of real-world applications in neurology and neurosurgery. Recent work is focused on the development of modern deep learning models for inference in medical image analysis in the presence of pathological structures (e.g. lesions, tumours), including: estimating and propagating uncertainties, knowledge distillation, interpretability/explainability, domain adaptation, learning and adapting to cohort biases in aggregated datasets, self-supervision, active learning, multi-modal predictions (e.g. merging clinical and imaging), etc. for problems ranging from segmentation, detection and probabilistic lesion count estimation to precision medicine based on patient brain images. In addition to theoretical advances, the outcome of this research has already led to concrete improvements in patient care. For example, probabilistic graphical machine learning algorithms developed by her team for Multiple Sclerosis (MS) lesion detection and segmentation have been used in the clinical trial analysis of most of the new multiple sclerosis drugs currently used worldwide. These advances have been made possible through strong collaborations with medical imaging, machine learning and computer vision researchers worldwide, as well as clinicians and several industrial partners.