Tal Arbel

Biography

Tal Arbel is a professor in the Department of Electrical and Computer Engineering at McGill University, where she is the director of the Probabilistic Vision Group and Medical Imaging Lab in the Centre for Intelligent Machines.

She is also a Canada CIFAR AI Chair, an associate academic member of Mila – Quebec Artificial Intelligence Institute and an associate member of the Goodman Cancer Research Centre.

Arbel’s research focuses on the development of probabilistic deep learning methods in computer vision and medical image analysis for a wide range of real-world applications, with a focus on neurological diseases.

She is a recipient of the 2019 McGill Engineering Christophe Pierre Research Award and a Fellow of the Canadian Academy of Engineering. She regularly serves on the organizing team of major international conferences in computer vision and in medical image analysis (e.g. MICCAI, MIDL, ICCV, CVPR). She is currently the Editor-in-Chief and co-founder of the arXiv overlay journal: Machine Learning for Biomedical Imaging (MELBA).

Current Students

Cornelius Crijnen

Collaborating researcher - Université de Montréal

Nima Fathi

Master's Research - McGill University

Gian Favero

Master's Research - McGill University

Elizabeth Laura Janes

Master's Research - McGill University

Emily Kaczmarek

PhD - McGill University

Amar Kumar

PhD - McGill University

Mohamed Mohamed

Master's Research - McGill University

Toky Raharison Ralambomihanta

Undergraduate - McGill University

Ryan Rezai

Master's Research - McGill University

Parham Saremi

Master's Research - McGill University

PRISM: An Explainable Generative AI Model for Medical Imaging

Xing Shen

Master's Research - McGill University

Master's Research - McGill University

Blog Posts

Image of an Xray and the DDIM process to generate counterfactual version of Xrays

July 1, 2025

Amar Kumar

Anita Kriz

Mohammed Havaei

Tal Arbel

Read the article

Publications

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas

Mauricio Reyes

Andras Jakab

Stefan. Bauer

Markus Rempfler

Alessandro Crimi

Russell T. Shinohara

Christoph Berger

Sung-min Ha

Martin Rozycki

Marcel W. Prastawa

Esther Alberts

Jana Lipková

John Freymann

Justin Kirby

Michel Bilello

Hassan M. Fathallah-Shaykh

Roland Wiest

J. Kirschke

Benedikt Wiestler … (see 31 more)

Rivka R. Colen

Aikaterini Kotrotsou

Pamela LaMontagne

D. Marcus

Mikhail Milchenko

Arash Nazeri

Marc-André Weber

Abhishek Mahajan

Ujjwal Baid

Dongjin Kwon

Manu Agarwal

Mahbubul Alam

Alberto Albiol

A. Albiol

Alex A. Varghese

T. Tuan

Aaron J. Avery

Bobade Pranjal

Subhashis Banerjee

Thomas H. Batchelder

Nematollah Batmanghelich

Enzo Battistella

Martin Bendszus

E. Benson

Jose Bernal

George Biros

Mariano Cabezas

Siddhartha Chandra

Yi-Ju Chang

et al.

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneo… (see more)us histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

2018-11-05

ArXiv (preprint)

Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation

Tanya Nair

Doina Precup

Douglas Arnold

2018-09-26

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (published)

Nazanin Mohammadi Sepahvand

CNN Prediction of Future Disease Activity for Multiple Sclerosis Patients from Baseline MRI and Lesion Labels

Tal Hassner

Douglas Arnold

2018-09-16

BrainLes@MICCAI (published)

3D U-Net for Brain Tumour Segmentation

Raghav Mehta

2018-09-16

BrainLes@MICCAI (published)

How to Exploit Weaknesses in Biomedical Challenge Design and Organization

Annika Reinke

Matthias Eisenmann

Sinan Onogur

Marko Stankovic

Patrick Scholz

Peter M. Full

Hrvoje Bogunovic

Bennett Landman

Oskar Maier

Bjoern Menze

Gregory C. Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

F. V. D. Sommen

Guoyan Zheng

Henning Müller

Michal Kozubek

Andrew P. Bradley

Pierre Jannin … (see 2 more)

Annette Kopp-Schneider

Lena Maier-Hein

2018-09-13

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (published)

RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours

Raghav Mehta

2018-09-12

Simulation and Synthesis in Medical Imaging (published)

Structured deep Fisher pruning for efficient facial trait classification

Qing Tian

James J. Clark

2018-09-01

Image and Vision Computing (published)

Fisher Pruning of Deep Nets for Facial Trait Classification

Qing Tian

James J. Clark

Although deep nets have resulted in high accuracies for various visual tasks, their computational and space requirements are prohibitively h… (see more)igh for inclusion on devices without high-end GPUs. In this paper, we introduce a neuron/filter level pruning framework based on Fisher's LDA which leads to high accuracies for a wide array of facial trait classification tasks, while significantly reducing space/computational complexities. The approach is general and can be applied to convolutional, fully-connected, and module-based deep structures, in all cases leveraging the high decorrelation of neuron activations found in the pre-decision layer and cross-layer deconv dependency. Experimental results on binary and multi-category facial traits from the LFWA and Adience datasets illustrate the framework's comparable/better performance to state-of-the-art pruning approaches and compact structures (e.g. SqueezeNet, MobileNet). Ours successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG-16, 82% for GoogLeNet) and with significant FLOP reductions (83% for VGG-16, 64% for GoogLeNet).

2018-03-21

ArXiv (preprint)

Task-specific Deep LDA pruning of neural networks

Qing Tian

James J. Clark

With deep learning's success, a limited number of popular deep nets have been widely adopted for various vision tasks. However, this usually… (see more) results in unnecessarily high complexities and possibly many features of low task utility. In this paper, we address this problem by introducing a task-dependent deep pruning framework based on Fisher's Linear Discriminant Analysis (LDA). The approach can be applied to convolutional, fully-connected, and module-based deep network structures, in all cases leveraging the high decorrelation of neuron motifs found in the pre-decision layer and cross-layer deconv dependency. Moreover, we examine our approach's potential in network architecture search for specific tasks and analyze the influence of our pruning on model robustness to noises and adversarial attacks. Experimental results on datasets of generic objects, as well as domain specific tasks (CIFAR100, Adience, and LFWA) illustrate our framework's superior performance over state-of-the-art pruning approaches and fixed compact nets (e.g. SqueezeNet, MobileNet). The proposed method successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG16, up to 82% for the already compact InceptionNet) and with significant FLOP reductions (83% for VGG16, up to 64% for InceptionNet). Through pruning, we can also derive smaller, but more accurate and more robust models suitable for the task.

Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight cases

Ian J. Gerard

Marta Kersten-Oertel

Simon Drouin

Jeffery A. Hall

Kevin Petrecca

Dante De Nigris

Daniel A. Di Giovanni

D. Louis Collins

Abstract. We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented … (see more)reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient’s cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.

2018-01-26

Journal of Medical Imaging (published)

Challenging Conventional Segmentation Evaluation Metrics Focal Pathology ( Lesion and Tumour ) Segmentation from Patient Images

How can we do better ? Pitfalls in biomedical challenge design and how to address them

Annika Reinke

Matthias Eisenmann

Sinan Onogur

Marko Stankovic

Patrick Scholz