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

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

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

Zahra TehraniNasab

Master's Research - McGill University

Mona Wang Wang

Undergraduate - McGill University

monaw7777@gmail.com

Ritchie Yu

Undergraduate - McGill University

Publications

Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning

Jean-Pierre R. Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Julien Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Arnold

2022-09-26

Nature Communications (published)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results

Raghav Mehta

Angelos Filos

Ujjwal Baid

Chiharu Sako

Richard McKinley

Michael Rebsamen

Katrin Dätwyler

Raphael Meier

Piotr Radojewski

Gowtham Krishnan Murugesan

Sahil Nalawade

Chandan Ganesh

Benjamin C. Wagner

Fang Frank Yu

Baowei Fei

Ananth J. Madhuranthakam

Joseph A. Maldjian

Laura Daza

Catalina Gómez

Pablo Arbeláez … (see 72 more)

Chengliang Dai

Shuo Wang

Hadrien Reynaud

Yuanhan Mo

Elsa Angelini

Yike Guo

Wenjia Bai

Subhashis Banerjee

Linmin Pei

Murat AK

Sarahi Rosas-González

Ilyess Zemmoura

Clovis Tauber

Minh H. Vu

Tufve Nyholm

Tommy Löfstedt

Laura Mora Ballestar

Veronica Vilaplana

Hugh McHugh

Gonzalo Maso Talou

Alan Wang

Jay Patel

Ken Chang

Katharina Hoebel

Mishka Gidwani

Nishanth Arun

Sharut Gupta

Mehak Aggarwal

Praveer Singh

Elizabeth R. Gerstner

Jayashree Kalpathy-Cramer

Nicolas Boutry

Alexis Huard

Lasitha Vidyaratne

Md Monibor Rahman

Khan M. Iftekharuddin

Joseph Chazalon

Elodie Puybareau

Guillaume Tochon

Jun Ma

Mariano Cabezas

Xavier Llado

Arnau Oliver

Liliana Valencia

Sergi Valverde

Mehdi Amian

Mohammadreza Soltaninejad

Andriy Myronenko

Ali Hatamizadeh

Xue Feng

Quan Dou

Nicholas Tustison

Craig Meyer

Nisarg A. Shah

Sanjay Talbar

Marc-André Weber

Abhishek Mahajan

Andras Jakab

Roland Wiest

Hassan M. Fathallah-Shaykh

Arash Nazeri

Mikhail Milchenko

Daniel Marcus

Aikaterini Kotrotsou

Rivka R. Colen

John Freymann

Justin Kirby

Christos Davatzikos

Bjoern Menze

Spyridon Bakas

Yarin Gal

2022-08-26

Machine Learning for Biomedical Imaging (published)

Heatmap Regression for Lesion Detection using Pointwise Annotations

Chelsea Myers-Colet

Julien Schroeter

Douglas Arnold

In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as… (see more) a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.

2022-08-11

ArXiv (preprint)

Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers

Amar Kumar

Anjun Hu

Brennan Nichyporuk

Jean-Pierre R. Falet

Douglas Arnold

Sotirios A. Tsaftaris

2022-08-03

ArXiv (preprint)

Information Gain Sampling for Active Learning in Medical Image Classification

Raghav Mehta

Changjian Shui

Brennan Nichyporuk

2022-08-01

ArXiv (preprint)

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)

openreview.net

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)

openreview.net

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.

openreview.net

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)