AI & Applications
Modelling and Propagating Uncertainties in Machine Learning for Medical Images Acquired from Patients with Neurological Diseases - Tal Arbel, Professor, McGill University

Tal Arbel image

DATE: Tue, September 21, 2021 - 8:30 am

LOCATION: Please register to receive the Zoom link


This event is co-hosted by the Centre for Artificial Intelligence Decision-making and Action (CAIDA) and the UBC MS Connect Education Program. 

Please register for this event here.


Although deep learning (DL) models have been shown to outperform other frameworks for a variety of medical contexts, inference in the presence of pathology in medical images presents challenges to popular networks. Errors in deterministic outputs lead to distrust by clinicians and hinders the adoption of DL methods in the clinic. Moreover, given that medical image analysis typically requires a sequence of inference tasks to be performed, this results in an accumulation of errors over the sequence of outputs. This talk will describe recent work exploring (MC-dropout) measures of uncertainty in DL lesion and tumour detection and segmentation models in patient images and illustrate how propagating uncertainties across cascaded medical imaging tasks can improve DL inference. The models are successfully applied to large-scale, multi-scanner, multi-center clinical trial datasets of patients with Multiple Sclerosis and to the MICCAI BRaTs brain tumour segmentation challenge datasets.  Finally, we describe a new hierarchical adversarial knowledge distillation network (HAD-Net) that improves enhanced tumour segmentation in the absence of post-contrast enhanced images (e.g. post Gadolinium injection). We show that the estimated uncertainties associated with the HAD-Net outputs do correlate with segmentation errors, paving the way for clinical review and potentially for future integration into clinical workflow. 



Tal Arbel is a Professor in the Department of Electrical and Computer Engineering, where she is the Director of the Probabilistic Vision Group and Medical Imaging Lab in the Centre for Intelligent Machines, McGill University. She is a Canada CIFAR AI Chair and Associate member of MILA (Montreal Institute for Learning Algorithms) and of the Goodman Cancer Research Centre. Prof. Arbel’s research focuses on development of probabilistic machine learning methods in computer vision and medical image analysis, with a wide range of real-world applications in neurology and neurosurgery. For example, the 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 MS  drugs currently used worldwide. She is a recipient of the 2019 McGill Engineering Christophe Pierre Research Award. She regularly serves on the organizing team of major international conferences in both fields (e.g.  MICCAI, MIDL, ICCV, CVPR). She was an Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and Computer Vision and Image Understanding (CVIU) and is now the Editor-in-Chief of a newly launched arXiv overlay journal: Machine Learning for Biomedical Imaging (MELBA). 


Please register for this event here.

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