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 regularly serves on organizing committees for major international conferences in computer vision and medical image analysis, including for the Medical Image Computing and Computer-Assisted Intervention Society/MICCAI, the Medical Imaging with Deep Learning/MIDL, the International Conference on Computer Vision/ICCV or the Computer Vision and Pattern Recognition Conference/CVPR). She co-founded the arXiv overlay journal, Machine Learning for Biomedical Imaging (MELBA) and is currently its editor-in-chief.

Current Students

Berardino Barile

Postdoctorate - McGill University

Joshua Durso-finley

PhD - McGill University

Nima Fathi

Master's Research - McGill University

Gian Favero

Master's Research - McGill University

elizabeth.janes@mila.quebec

Ethan Honey

PhD - McGill University

ethan.honey@mila.quebec

Elizabeth Laura Janes

Master's Research - McGill University

Emily Kaczmarek

PhD - McGill University

emily.kaczmarek@mail.mcgill.ca

Anita Kriz

Master's Research - McGill University

Amar Kumar

PhD - McGill University

Mohamed Mohamed

Master's Research - McGill University

Parham Saremi

Master's Research - McGill University

Xing Shen

Master's Research - McGill University

Master's Research - McGill University

Mona Wang Wang

Undergraduate - McGill University

monaw7777@gmail.com

Ritchie Yu

Undergraduate - McGill University

Publications

GP.2 Deep learning prediction of response to disease modifying therapy in primary progressive multiple sclerosis

JR Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Julien Schroeter

Francesca Bovis

M Sormani

D Precup

DL Arnold

Background: Only one disease modifying therapy (DMT), ocrelizumab, was found to slow disability progression in primary progressive multiple … (see more)sclerosis (PPMS). Modeling the conditional average treatment effect (CATE) using deep learning could identify individuals more responsive to DMTs, allowing for predictive enrichment to increase the power of future clinical trials. Methods: Baseline clinical and MRI data were acquired as part of three placebo-controlled randomized clinical trials: ORATORIO (ocrelizumab), OLYMPUS (rituximab) and ARPEGGIO (laquinimod). Data from ORATORIO and OLYMPUS was separated into a training (70%) and testing (30%) set, while ARPEGGIO served as additional validation. An ensemble of multitask multilayer perceptrons was trained to predict the rate of disability progression on both treatment and placebo to estimate CATE. Results: The model could separate individuals based on their predicted treatment effect. The top 25% of individuals predicted to respond most have a larger effect size (HR 0.442, p=0.0497) than the entire group (HR 0.787, p=0.292). The model could also identify responders to laquinimod. A simulated study where the 50% most responsive individuals are randomized would require 6-times less participants to detect a significant effect. Conclusions: Individuals with PPMS who respond favourably to DMTs can be identified using deep learning based on their baseline clinical and imaging characteristics.

2022-06-24

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques (published)

Metrics Reloaded - A new recommendation framework for biomedical image analysis validation

Annika Reinke

Lena Maier-Hein

Evangelia Christodoulou

Ben Glocker

Patrick Scholz

Fabian Isensee

Jens Kleesiek

Michal Kozubek

Mauricio Reyes

Michael Alexander Riegler

Manuel Wiesenfarth

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

Ali Emre Kavur

TIM RÄDSCH

Minu D. Tizabi

LAURA ACION

Michela Antonelli

Tal Arbel … (see 48 more)

Spyridon Bakas

Peter Bankhead

Arriel Benis

M. Jorge Cardoso

Veronika Cheplygina

Beth A Cimini

Gary S. Collins

Keyvan Farahani

Bram van Ginneken

Fred A Hamprecht

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Pierre Jannin

Charles Kahn

ALEXANDROS KARARGYRIS

Alan Karthikesalingam

Hannes Kenngott

Annette Kopp-Schneider

Anna Kreshuk

Tahsin Kurc

Bennett Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

David Moher

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Felix Nickel

Jens Petersen

NASIR RAJPOOT

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA SHETTY

Maarten van Smeden

Carole H. Sudre

Ronald M. Summers

Abdel A. Taha

Sotirios A. Tsaftaris

Ben Van Calster

Gael Varoquaux

Paul F Jaeger

Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There ar… (see more)e major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.

2022-05-09

MIDL.io/2022/Conference/Short (published)

Deep Learning Prediction of Response to Disease Modifying Therapy in Primary Progressive Multiple Sclerosis (P1-1.Virtual)

Jean-Pierre R. Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Julien Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Arnold

2022-05-03

Neurology (published)

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Joshua D. Durso-Finley

Jean-Pierre R. Falet

Brennan Nichyporuk

Douglas Arnold

Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side … (see more)effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients which (a) predicts future new and enlarging T2 weighted (NE-T2) lesion counts on follow-up MRI on multiple treatments and (b) estimates the conditional average treatment effect (CATE), as defined by the predicted future suppression of NE-T2 lesions, between different treatment options relative to placebo. Our model is validated on a proprietary federated dataset of 1817 multi-sequence MRIs acquired from MS patients during four multi-centre randomized clinical trials. Our framework achieves high average precision in the binarized regression of future NE-T2 lesions on five different treatments, identifies heterogeneous treatment effects, and provides a personalized treatment recommendation that accounts for treatment-associated risk (side effects, patient preference, administration difficulties).

2022-02-28

MIDL.io/2022/Conference (published)

On Learning Fairness and Accuracy on Multiple Subgroups

Changjian Shui

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Boyu Wang

Christian Gagné

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (see more)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

Estimating treatment effect for individuals with progressive multiple sclerosis using deep learning

JR Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Jan Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Arnold

2021-11-01

medRxiv (preprint)

Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation

Brennan Nichyporuk

Jillian L. Cardinell

Justin Szeto

Raghav Mehta

Sotirios A. Tsaftaris

Douglas Arnold

2021-09-21

Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health (published)

arxiv.org

HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images

Saverio Vadacchino

Raghav Mehta

Nazanin Mohammadi Sepahvand

Brennan Nichyporuk

James J. Clark

Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, ac… (see more)curate segmentation requires the inclusion of medical images (e.g., T1 post-contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline adversarial knowledge distillation (KD) technique, whereby a pre-trained teacher segmentation network, with access to all MRI sequences, teaches a student network, via hierarchical adversarial training, to better overcome the large domain shift presented when crucial images are absent during inference. In particular, we apply HAD-Net to the challenging task of enhancing tumour segmentation when access to post-contrast imaging is not available. The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET). The network also shows improvements in tumour core (TC) Dice scores. Finally, the network outperforms both the baseline student network and AD-Net in terms of uncertainty quantification for enhancing tumour segmentation based on the BraTS 2019 uncertainty challenge metrics. Our code is publicly available at: https://github.com/SaverioVad/HAD_Net

2021-08-25

Proceedings of the Fourth Conference on Medical Imaging with Deep Learning (published)

proceedings.mlr.press

Common limitations of performance metrics in biomedical image analysis

Annika Reinke

Matthias Eisenmann

Minu Dietlinde Tizabi

Carole H. Sudre

TIM RÄDSCH

Michela Antonelli

Spyridon Bakas

M. Jorge Cardoso

Veronika Cheplygina

Keyvan Farahani

Ben Glocker

DOREEN HECKMANN-NÖTZEL

Fabian Isensee

Pierre Jannin

Charles Kahn

Jens Kleesiek

Tahsin Kurc

Michal Kozubek

Bennett Landman … (see 15 more)

GEERT LITJENS

Klaus Maier-Hein

Anne Lousise Martel

Bjoern Menze

Henning Müller

Jens Petersen

Mauricio Reyes

Nicola Rieke

Bram Stieltjes

Ronald M. Summers

Sotirios A. Tsaftaris

Bram van Ginneken

Annette Kopp-Schneider

Paul Jäger

Lena Maier-Hein

2021-05-11

MIDL.io/2021/Conference/Short (poster)

Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting

Brennan Nichyporuk

Justin Szeto

Douglas Arnold

There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patie… (see more)nt images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.

2021-05-11

MIDL.io/2021/Conference/Short (poster)

Common Limitations of Image Processing Metrics: A Picture Story

Annika Reinke

Matthias Eisenmann

Minu Dietlinde Tizabi

Carole H. Sudre

TIM RÄDSCH

Michela Antonelli

Spyridon Bakas

M. Jorge Cardoso

Veronika Cheplygina

Keyvan Farahani

B. Glocker

DOREEN HECKMANN-NÖTZEL

Fabian Isensee

Pierre Jannin

Charles E. Jr. Kahn

Jens Kleesiek

Tahsin Kurc

Michal Kozubek

Bennett Landman … (see 14 more)

GEERT LITJENS

Klaus Maier-Hein

Bjoern Menze

Henning Müller

Jens Petersen

Mauricio Reyes

Nicola Rieke

Bram Stieltjes

R. Summers

Sotirios A. Tsaftaris

Bram van Ginneken

Annette Kopp-Schneider

PAUL F. JÄGER

Lena Maier-Hein

2021-04-12

ArXiv (preprint)

arxiv.org

Task dependent deep LDA pruning of neural networks

Qing Tian

James J. Clark

2021-02-01

Computer Vision and Image Understanding (published)