Tal Arbel

Biographie

Tal Arbel est professeure titulaire au Département de génie électrique et informatique de l’Université McGill, où elle dirige le groupe de vision probabiliste et le laboratoire d'imagerie médicale du Centre sur les machines intelligentes.

Elle est titulaire d'une chaire en IA Canada-CIFAR et membre associée de Mila – Institut québécois d’intelligence artificielle ainsi que du Centre de recherche sur le cancer Goodman. Les recherches de la professeure Arbel portent sur le développement de méthodes probabilistes d'apprentissage profond dans les domaines de la vision par ordinateur et de l’analyse d'imagerie médicale pour un large éventail d'applications dans le monde réel, avec un accent particulier sur les maladies neurologiques.

Elle a remporté le prix de la recherche Christophe Pierre 2019 de McGill Engineering et est Fellow à l'Académie canadienne d'ingénierie. Elle fait régulièrement partie de l'équipe organisatrice de grandes conférences internationales sur la vision par ordinateur et l'analyse d'imagerie médicale (par exemple celles de la Medical Image Computing and Computer-Assisted Intervention Society/MICCAI et de Medical Imaging with Deep Learning/MIDL, l’International Conference on Computer Vision/ICCV ou encore la Conference on Computer Vision and Pattern Recognition/CVPR). Elle est rédactrice en chef et cofondatrice de la revue Machine Learning for Biomedical Imaging (MELBA).

Étudiants actuels

Cornelius Crijnen

Doctorat - McGill

Nima Fathi

Collaborateur·rice alumni - McGill

Hossein Jafarinia

Collaborateur·rice de recherche - McGill University

Elizabeth Laura Janes

Maîtrise recherche - McGill

Emily Kaczmarek

Doctorat - McGill

Amar Kumar

Doctorat - McGill

Mohamed Mohamed

Maîtrise recherche - McGill

Alexander Natalie

Collaborateur·rice de recherche - N/A

Toky Raharison Ralambomihanta

Baccalauréat - McGill

Ryan Rezai

Maîtrise recherche - McGill

Parham Saremi

Maîtrise recherche - McGill

Xing Shen

Maîtrise recherche - McGill

Maîtrise recherche - McGill

Minh To

Collaborateur·rice de recherche - UBC

PRISM : Un modèle d'IA générative explicable pour l'imagerie médicale

Billets de blogue

Image of an Xray and the DDIM process to generate counterfactual version of Xrays

1 juillet 2025

par

Amar Kumar

Anita Kriz

Mohammed Havaei

Tal Arbel

Lire l'article

Publications

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

M. Jorge Cardoso

Veronika Cheplygina

JIANXU CHEN

Evangelia Christodoulou … (voir 59 de plus)

BETH A. CIMINI

Gary S. Collins

Keyvan Farahani

LUCIANA FERRER

Adrian Galdran

Bram van Ginneken

Ben Glocker

PATRICK GODAU

Robert Cary Haase

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Fabian Isensee

Pierre Jannin

CHARLES E. KAHN

DAGMAR KAINMUELLER

BERNHARD KAINZ

Alexandros Karargyris

Alan Karthikesalingam

H. Kenngott

Jens Kleesiek

Florian Kofler

THIJS KOOI

Annette Kopp-Schneider

Michal Kozubek

Anna Kreshuk

Tahsin Kurc

BENNETT A. LANDMAN

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne L. Martel

Peter Mattson

ERIK MEIJERING

Bjoern Menze

KAREL G.M. MOONS

Henning Müller

Brennan Nichyporuk

Felix Nickel

Jens Petersen

SUSANNE M. RAFELSKI

NASIR RAJPOOT

Mauricio Reyes

MICHAEL A. RIEGLER

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

SHRAVYA SHETTY

M. Smeden

Ronald M. Summers

Abdel Aziz Taha

ALEKSEI TIULPIN

Sotirios A. Tsaftaris

Ben Van Calster

Gael Varoquaux

Manuel Wiesenfarth

ZIV R. YANIV

PAUL F. JÄGER

Lena Maier-Hein

2024-02-12

Nature methods (publié)

arxiv.org

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

Mohammad Havaei

Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (voir plus)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.

2024-01-01

MIDL (publié)

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 (publié)

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… (voir plus)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 (publié)

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… (voir plus)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 (publié)

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

Changjian Shui

Justin Szeto

Raghav Mehta

Douglas Arnold

2023-10-08

OpenReview.net/Archive (publié)

Grow-push-prune: Aligning deep discriminants for effective structural network compression

Qing Tian

James J. Clark

2023-06-01

Computer Vision and Image Understanding (publié)

arxiv.org

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Junde Wu

Rao Fu

Huihui Fang

Yuanpei Liu

Zhao-Yang Wang

Yanwu Xu

Yueming Jin

The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in var… (voir plus)ious segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. In this paper, instead of fine-tuning the SAM model, we propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model using a light yet effective adaptation technique. In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. We conduct comprehensive evaluation experiments on 17 medical image segmentation tasks across various image modalities. Med-SA outperforms several state-of-the-art (SOTA) medical image segmentation methods, while updating only 2\% of the parameters. Our code is released at https://github.com/KidsWithTokens/Medical-SAM-Adapter.

2023-04-25

ArXiv (prépublication)

arxiv.org

Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis

Raghav Mehta

Changjian Shui

Although deep learning (DL) models have shown great success in many medical image analysis tasks, deployment of the resulting models into r… (voir plus)eal clinical contexts requires: (1) that they exhibit robustness and fairness across different sub-populations, and (2) that the confidence in DL model predictions be accurately expressed in the form of uncertainties. Unfortunately, recent studies have indeed shown significant biases in DL models across demographic subgroups (e.g., race, sex, age) in the context of medical image analysis, indicating a lack of fairness in the models. Although several methods have been proposed in the ML literature to mitigate a lack of fairness in DL models, they focus entirely on the absolute performance between groups without considering their effect on uncertainty estimation. In this work, we present the first exploration of the effect of popular fairness models on overcoming biases across subgroups in medical image analysis in terms of bottom-line performance, and their effects on uncertainty quantification. We perform extensive experiments on three different clinically relevant tasks: (i) skin lesion classification, (ii) brain tumour segmentation, and (iii) Alzheimer's disease clinical score regression. Our results indicate that popular ML methods, such as data-balancing and distributionally robust optimization, succeed in mitigating fairness issues in terms of the model performances for some of the tasks. However, this can come at the cost of poor uncertainty estimates associated with the model predictions. This tradeoff must be mitigated if fairness models are to be adopted in medical image analysis.

2023-04-04

MIDL.io/2023/Conference (poster)

Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis

Raghav Mehta

Changjian Shui

2023-04-04

MIDL.io/2023/Conference (poster)

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 … (voir plus)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 (publié)

Segmentation-Consistent Probabilistic Lesion Counting

Julien Schroeter

Chelsea Myers-Colet

Douglas Arnold

Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical ima… (voir plus)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 (publié)