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

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

Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs

Joshua D. Durso-Finley

Berardino Barile

Jean-Pierre R. Falet

Douglas Arnold

Nick Pawlowski

Personalized medicine based on medical images, including predicting future individualized clinical disease progression and treatment respons… (see more)e, would have an enormous impact on healthcare and drug development, particularly for diseases (e.g. multiple sclerosis (MS)) with long term, complex, heterogeneous evolutions and no cure. In this work, we present the first stochastic causal temporal framework to model the continuous temporal evolution of disease progression via Neural Stochastic Differential Equations (NSDE). The proposed causal inference model takes as input the patient's high dimensional images (MRI) and tabular data, and predicts both factual and counterfactual progression trajectories on different treatments in latent space. The NSDE permits the estimation of high-confidence personalized trajectories and treatment effects. Extensive experiments were performed on a large, multi-centre, proprietary dataset of patient 3D MRI and clinical data acquired during several randomized clinical trials for MS treatments. Our results present the first successful uncertainty-based causal Deep Learning (DL) model to: (a) accurately predict future patient MS disability evolution (e.g. EDSS) and treatment effects leveraging baseline MRI, and (b) permit the discovery of subgroups of patients for which the model has high confidence in their response to treatment even in clinical trials which did not reach their clinical endpoints.

2024-10-03

Lecture Notes in Computer Science (published)

DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations

Nima Fathi

Amar Kumar

Brennan Nichyporuk

Mohammad Havaei

Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (see more)with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.

2024-06-06

MIDL.io/2024/Conference (oral)

HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling

Daniel Duenias

Brennan Nichyporuk

Tammy Riklin-Raviv

The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Reco… (see more)rds (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and the generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multiclass Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods. The code, enclosed to this manuscript will be made publicly available.

2024-03-20

ArXiv (preprint)

DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations

Nima Fathi

Amar Kumar

Brennan Nichyporuk

Mohammad Havaei

2024-02-13

MIDL.io/2024/Conference/Submission (unknown)

Metrics reloaded: Pitfalls and recommendations for image analysis validation

Lena Maier-Hein

Annika Reinke

Evangelia Christodoulou

Ben Glocker

PATRICK GODAU

Fabian Isensee

Jens Kleesiek

Michal Kozubek

Mauricio Reyes

MICHAEL A. RIEGLER

Manuel Wiesenfarth

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

A. EMRE KAVUR

TIM RÄDSCH

Minu Dietlinde Tizabi

LAURA ACION

Michela Antonelli

Tal Arbel … (see 47 more)

Spyridon Bakas

Peter Bankhead

Allison Benis

M. Jorge Cardoso

Veronika Cheplygina

BETH A. CIMINI

Gary S. Collins

Keyvan Farahani

Bram van Ginneken

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Pierre Jannin

CHARLES E. KAHN

ALEXANDROS KARARGYRIS

Alan Karthikesalingam

H. Kenngott

Annette Kopp-Schneider

Anna Kreshuk

Tahsin Kurc

Bennett Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

David Moher

KAREL G.M. MOONS

Henning Müller

Felix Nickel

Brennan Nichyporuk

Jens Petersen

NASIR RAJPOOT

Nicola Rieke

Julio Saez-Rodriguez

Clarisa S'anchez Guti'errez

SHRAVYA SHETTY

M. Smeden

Carole H. Sudre

Ronald M. Summers

Abdel Aziz Taha

Sotirios A. Tsaftaris

B. Calster

Gael Varoquaux

PAUL F. JÄGER

2024-02-12

Nature Methods (published)

Understanding metric-related pitfalls in image analysis validation

Annika Reinke

Minu Dietlinde Tizabi

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

A. EMRE KAVUR

TIM RÄDSCH

Carole H. Sudre

LAURA ACION

Michela Antonelli

Spyridon Bakas

Allison Benis

Arriel Benis

Matthew Blaschko

FLORIAN BUETTNER

Florian Buttner

M. Jorge 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

ALEXANDROS KARARGYRIS

Jens Kleesiek

Florian Kofler

THIJS KOOI

Annette Kopp-Schneider

Alan Karthikesalingam

H. Kenngott

Michal Kozubek

Anna Kreshuk

Tahsin Kurc

Bennett A. Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

ERIK MEIJERING

Bjoern Menze

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Peter Mattson

Felix Nickel

Jens Petersen

SUSANNE M. RAFELSKI

NASIR RAJPOOT

Mauricio Reyes

MICHAEL A. RIEGLER

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA 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

2024-02-12

Nature methods (published)

Current AI applications in neurology: Brain imaging

Joshua D. Durso-Finley

Jean-Pierre R. Falet

Raghav Mehta

Douglas Arnold

Nick Pawlowski

2023-12-01

Journal of the Neurological Sciences (published)

Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles

Xing Shen

Hengguan Huang

Brennan Nichyporuk

2023-10-24

ArXiv (preprint)

Debiasing Counterfactuals in the Presence of Spurious Correlations

Amar Kumar

Nima Fathi

Raghav Mehta

Brennan Nichyporuk

Jean-Pierre R. Falet

Sotirios A. Tsaftaris

Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correl… (see more)ations (i.e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest. This would thereby limit their ability to generalize across the population. Explainability based on counterfactual image generation can be used to expose the confounders but does not provide a strategy to mitigate the bias. In this work, we introduce the first end-to-end training framework that integrates both (i) popular debiasing classifiers (e.g. distributionally robust optimization (DRO)) to avoid latching onto the spurious correlations and (ii) counterfactual image generation to unveil generalizable imaging markers of relevance to the task. Additionally, we propose a novel metric, Spurious Correlation Latching Score (SCLS), to quantify the extent of the classifier reliance on the spurious correlation as exposed by the counterfactual images. Through comprehensive experiments on two public datasets (with the simulated and real visual artifacts), we demonstrate that the debiasing method: (i) learns generalizable markers across the population, and (ii) successfully ignores spurious correlations and focuses on the underlying disease pathology.

2023-10-09

Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging (published)

Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models

Joshua D. Durso-Finley

Jean-Pierre R. Falet

Raghav Mehta

Douglas Arnold

Nick Pawlowski

Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve the… (see more)ir clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.

2023-10-08

OpenReview.net/Archive (published)

Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis

Changjian Shui

Justin Szeto

Raghav Mehta

Douglas Arnold