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

BIAS: Transparent reporting of biomedical image analysis challenges

Lena Maier-Hein

Annika Reinke

Michal Kozubek

Anne L. Martel

Matthias Eisenmann

Allan Hanbury

Pierre Jannin

Henning Müller

Sinan Onogur

Julio Saez-Rodriguez

Bram van Ginneken

Annette Kopp-Schneider

Bennett Landman

2020-08-21

Medical Image Analysis (published)

arxiv.org

Medical Imaging with Deep Learning: MIDL 2020 - Short Paper Track

Ismail Ben Ayed

Marleen de Bruijne

Maxime Descoteaux

Hervé Lombaert

Chris Pal

This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (M… (see more)IDL 2020), held in Montreal, Canada, 6-9 July 2020. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2020 Full Paper Track are published in the Proceedings of Machine Learning Research (PMLR).

2020-06-29

ArXiv (preprint)

arxiv.org

Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis

Aaron Carass

Snehashis Roy

Adrian Gherman

Jacob C. Reinhold

Andrew Jesson

Oskar Maier

Heinz Handels

Mohsen Ghafoorian

Bram Platel

Ariel Birenbaum

Hayit Greenspan

Dzung L. Pham

Ciprian M. Crainiceanu

Peter A. Calabresi

Jerry L. Prince

William R. Gray Roncal

Russell T. Shinohara

Ipek Oguz

2020-05-19

Scientific Reports (published)

Uncertainty Evaluation Metric for Brain Tumour Segmentation

Raghav Mehta

Angelos Filos

Yarin Gal

Nazanin Mohammadi Sepahvand

In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in … (see more)the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.

2020-04-17

MIDL.io/2020/Conference (published)

openreview.net

CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images

Douglas Arnold

Accurate detection and segmentation of new lesional activity in longitudinal Magnetic Resonance Images (MRIs) of patients with Multiple Scle… (see more)rosis (MS) is important for monitoring disease activity, as well as for assessing treatment effects. In this work, we present the first deep learning framework to automatically detect and segment new and enlarging (NE) T2w lesions from longitudinal brain MRIs acquired from relapsing-remitting MS (RRMS) patients. The proposed framework is an adapted 3D U-Net [1] which includes as inputs the reference multi-modal MRI and T2-weighted lesion maps, as well an attention mechanism based on the subtraction MRI (between the two timepoints) which serves to assist the network in learning to differentiate between real anatomical change and artifactual change, while constraining the search space for small lesions. Experiments on a large, proprietary, multi -center, multi-modal, clinical trial dataset consisting of 1677 multi-modal scans illustrate that network achieves high overall detection accuracy (detection AUC=.95), outperforming (1) a U-Net without an attention mechanism (de-tection AUC=.93), (2) a framework based on subtracting independent T2-weighted segmentations (detection AUC=.57), and (3) DeepMedic (detection AUC=.84) [2], particularly for small lesions. In addition, the method was able to accurately classify patients as active/inactive with (sensitivities of. 69 and specificities of. 97).

2020-04-03

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (published)

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference

Raghav Mehta

Thomas Christinck

Tanya Nair

Aurélie Bussy

Paul Lemaitre

Swapna Premasiri

Douglas Arnold

Manuela Costantino

Mallar Chakravarty

Yarin Gal

Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology pr… (see more)esents several challenges to common models. These challenges impede the integration of deep learning models into real clinical workflows, where the customary process of cascading deterministic outputs from a sequence of image-based inference steps (e.g. registration, segmentation) generally leads to an accumulation of errors that impacts the accuracy of downstream inference tasks. In this paper, we propose that by embedding uncertainty estimates across cascaded inference tasks, performance on the downstream inference tasks should be improved. We demonstrate the effectiveness of the proposed approach in three different clinical contexts: (i) We demonstrate that by propagating T2 weighted lesion segmentation results and their associated uncertainties, subsequent T2 lesion detection performance is improved when evaluated on a proprietary large-scale, multi-site, clinical trial dataset acquired from patients with Multiple Sclerosis. (ii) We show an improvement in brain tumour segmentation performance when the uncertainty map associated with a synthesised missing MR volume is provided as an additional input to a follow-up brain tumour segmentation network, when evaluated on the publicly available BraTS-2018 dataset. (iii) We show that by propagating uncertainties from a voxel-level hippocampus segmentation task, the subsequent regression of the Alzheimer’s disease clinical score is improved.

2019-10-17

UNSURE/CLIP@MICCAI (published)

Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI

Joshua D. Durso-Finley

Douglas Arnold

2019-10-17

BrainLes@MICCAI (published)

Nazanin Mohammadi Sepahvand

Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

Barleen Kaur

Paul Lemaitre

Raghav Mehta

Doina Precup

Douglas Arnold

2019-10-13

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data (published)

Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data

Adrian Tousignant

Paul Lemaitre

Doina Precup

Douglas Arnold

We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from… (see more) baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net (Szegedy et al., 2015) and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66±0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701± 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.

2019-05-24

International Conference on Medical Imaging with Deep Learning (published)

proceedings.mlr.press

Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields

Nagesh K. Subbanna

Deepthi Rajashekar

Bastian Cheng

Götz Thomalla

Jens Fiehler

Nils D. Forkert

Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized tr… (see more)ials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions from fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) datasets. After preprocessing of the datasets, a Bayesian technique based on Gabor textures extracted from the FLAIR signal intensities is utilized to generate a first estimate of the lesion segmentation. Using this initial segmentation, a customized voxel-level Markov random field model based on intensity as well as Gabor texture features is employed to refine the stroke lesion segmentation. The proposed method was developed and evaluated based on 151 multi-center datasets from three different databases using a leave-one-patient-out validation approach. The comparison of the automatically segmented stroke lesions with manual ground truth segmentation revealed an average Dice coefficient of 0.582, which is in the upper range of previously presented lesion segmentation methods using multi-modal MRI datasets. Furthermore, the results obtained by the proposed technique are superior compared to the results obtained by two methods based on convolutional neural networks and three phase level-sets, respectively, which performed best in the ISLES 2015 challenge using multi-modal imaging datasets. The results of the quantitative evaluation suggest that the proposed method leads to robust lesion segmentation results using FLAIR MRI datasets only as a follow-up sequence.

2019-05-24

Frontiers in Neurology (published)

Prediction of Progression in Multiple Sclerosis Patients

Adrian Tousignant

Paul Lemaitre

Doina Precup

Douglas Arnold

We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from… (see more) baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66 +- 0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701 +- 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.

2019-02-28

MIDL.io/2019/Conference (poster)

openreview.net

Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

Lena Maier-Hein

Matthias Eisenmann

Annika Reinke

Sinan Onogur

Marko Stankovic

Patrick Scholz

Hrvoje Bogunovic

Andrew P. Bradley

Aaron Carass

Carolin Feldmann

Alejandro F. Frangi

Peter M. Full

Bram van Ginneken

Allan Hanbury

Katrin Honauer

Michal Kozubek

Bennett Landman

Keno März

Oskar Maier … (see 18 more)

Klaus Maier-Hein

Bjoern Menze

Henning Müller

Peter F. Neher

Wiro Niessen

NASIR RAJPOOT

Gregory C. Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

Christian Stock

Danail Stoyanov

Abdel Aziz Taha

Fons van der Sommen

Ching-Wei Wang

Marc-André Weber

Guoyan Zheng

Pierre Jannin

Annette Kopp-Schneider

2019-01-30

Nature Communications (published)