Portrait of Tal Arbel

Tal Arbel

Core Academic Member
arbeltal@mila.quebec
Canada CIFAR AI Chair
Full Professor, McGill University, Department of Electrical and Computer Engineering
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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

Amar Kumar
PhD - McGill University
kumarama@mila.quebec
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Anita Kriz
Master's Research - McGill University
anita.kriz@mila.quebec
Berardino Barile
Postdoctorate - McGill University
berardino.barile@mila.quebec
Google Scholar
Emily Kaczmarek
PhD - McGill University
emily.kaczmarek@mila.quebec
Github
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Gian Favero
Master's Research - McGill University
gian.favero@mila.quebec
Website
Github
Joshua Durso-finley
PhD - McGill University
dursofij@mila.quebec
Mohamed Mohamed
Master's Research - McGill University
mohamed.mohamed@mila.quebec
Github
Nima Fathi
Master's Research - McGill University
nima.fathi@mila.quebec
Website
Github
Parham Saremi
Master's Research - McGill University
parham.saremi@mila.quebec
Github
Xing Shen
Master's Research - McGill University
xing.shen@mila.quebec
Website
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Zahra TehraniNasab
Master's Research - McGill University
zahra.tehraninasab@mila.quebec

Publications

Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis
Raghav Mehta
Changjian Shui
Tal Arbel
2023-04-04
MIDL.io/2023/Conference (poster)
doi.org
openreview.net
Understanding metric-related pitfalls in image analysis validation
Annika Reinke
Minu Dietlinde Tizabi
Michael Baumgartner
Matthias Eisenmann
Doreen Heckmann-Notzel
A. Emre Kavu
Tim Radsch
Carole H. Sudre
Laura C. Acion
Michela Antonelli
Tal Arbel
Spyridon Bakas
Allison Benis
Arriel Benis
Matthew Blaschko
Florian Buettner
Florian Buttner
M. Cardoso
Veronika Cheplygina
Jianxu Chen … (see 62 more)
Evangelia Christodoulou
Beth A. Cimini
Keyvan Farahani
Luciana Ferrer
Gary S. Collins
Adrian Galdran
Bram van Ginneken
Ben Glocker
Patrick Godau
Daniel A. Hashimoto
Michael M. Hoffman
Robert Cary Haase
Merel Huisman
Fabian Isensee
Pierre Jannin
Charles E. Kahn
Dagmar Kainmueller
Bernhard Kainz
A. Karargyris
Jens Kleesiek
Florian Kofler
Thijs Kooi
Annette Kopp-Schneider
Alan Karthikesalingam
H. Kenngott
Michal Kozubek
Anna Kreshuk
Tahsin Kurc
Bennett A. Landman
G. Litjens
Amin Madani
Klaus Maier-Hein
Anne L. Martel
Erik H. W. Meijering
Bjoern Menze
K. Moons
Henning Müller
Brennan Nichyporuk
Peter Mattson
Felix Nickel
Jens Petersen
Susanne M. Rafelski
Nasir M. Rajpoot
Mauricio Reyes
Michael A. Riegler
Nicola Rieke
Julio Saez-Rodriguez
Clara I. Sánchez
Shravya Jaganath Shetty
Ronald M. Summers
Abdel Aziz Taha
Aleksei Tiulpin
Sotirios A. Tsaftaris
Ben Van Calster
Gael Varoquaux
Ziv R Yaniv
M. Smeden
Paul F. Jäger
Lena Maier-Hein
B. Calster
Manuel Wiesenfarth
Ziv Rafael Yaniv
2023-02-09
arXiv.org (preprint)
doi.org
arxiv.org
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
Tal Arbel
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,...).
2022-12-04
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (published)
doi.org
openreview.net
Segmentation-Consistent Probabilistic Lesion Counting
Julien Schroeter
Chelsea Myers-Colet
Douglas Arnold
Tal Arbel
Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical ima… (see more)ging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach—which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting—is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks.
2022-12-04
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (published)
doi.org
openreview.net
Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors
Anjun Hu
Jean-Pierre R. Falet
Brennan Nichyporuk
Changjian Shui
Douglas Arnold
Sotirios A. Tsaftaris
Tal Arbel
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain l… (see more)esions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies.
2022-11-15
ArXiv (preprint)
doi.org
arxiv.org
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
Brennan Nichyporuk
Jillian L. Cardinell
Justin Szeto
Raghav Mehta
Jean-Pierre R. Falet
Douglas Arnold
Sotirios A. Tsaftaris
Tal Arbel
Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, wh… (see more)ere unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the "ground-truth" label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified.
2022-10-31
ArXiv (preprint)
doi.org
arxiv.org
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
Tal Arbel
Douglas Arnold
2022-09-26
Nature Communications (published)
doi.org
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
Tal Arbel
2022-08-26
Machine Learning for Biomedical Imaging (published)
doi.org
arxiv.org
Heatmap Regression for Lesion Detection using Pointwise Annotations
Chelsea Myers-Colet
Julien Schroeter
Douglas Arnold
Tal Arbel
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)
doi.org
arxiv.org
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
Tal Arbel
2022-08-03
ArXiv (preprint)
doi.org
arxiv.org
Information Gain Sampling for Active Learning in Medical Image Classification
Raghav Mehta
Changjian Shui
Brennan Nichyporuk
Tal Arbel
2022-08-01
ArXiv (preprint)
doi.org
arxiv.org
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
Tal Arbel
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)
doi.org