Portrait de Tal Arbel

Tal Arbel

Membre académique principal
arbeltal@mila.quebec
Chaire en IA Canada-CIFAR
Professeure titulaire, McGill University, Département de génie électrique et informatique
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage profond
Causalité
Modèles génératifs
Modèles probabilistes
Vision par ordinateur
Site web
Google Scholar
LinkedIn
X

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

Karl Bridi
Stagiaire de recherche - McGill
karl.bridi@mila.quebec
Github
LinkedIn
Cornelius Crijnen
Doctorat - McGill
cornelius.crijnen@mila.quebec
Site web
Github
Oscar Cruzhernandez
Stagiaire de recherche - McGill
oscar.cruzhernandez@mila.quebec
Charbel El Feghali
Stagiaire de recherche - McGill
charbel.elfeghali@mila.quebec
Carlotta Hoelzle
Stagiaire de recherche - McGill
carlotta.hoelzle@mila.quebec
Github
Elizabeth Laura Janes
Maîtrise recherche - McGill
elizabeth.janes@mila.quebec
Emily Kaczmarek
Doctorat - McGill
emily.kaczmarek@mail.mcgill.ca
Google Scholar
Yusong Li
Collaborateur·rice de recherche - McGill University
yusong.li@mila.quebec
Github
LinkedIn
Google Scholar
Yik Yu Ng
Stagiaire de recherche - McGill
yik.ng@mila.quebec
Toky Raharison Ralambomihanta
Stagiaire de recherche - McGill
tokiniaina.raharisonralambomihan@mail.mcgill.ca
Github
Ryan Rezai
Maîtrise recherche - McGill
ryan.rezai@mila.quebec
Rachel Ruddy
Stagiaire de recherche - McGill
rachel.ruddy@mila.quebec
Github
Minh To
Collaborateur·rice de recherche - UBC
tnnhatminh@gmail.com
Site web
Github
Google Scholar

Publications

Metrics reloaded: 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 … (voir 47 de plus)
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
Ben Van Calster
Gael Varoquaux
PAUL F. JÄGER
2024-02-11
Nature Methods (publié)
doi.org
arxiv.org
Understanding metric-related pitfalls in image analysis validation
Annika Reinke
Minu D. Tizabi
Michael Baumgartner
Matthias Eisenmann
DOREEN HECKMANN-NÖTZEL
A. EMRE KAVUR
TIM RÄDSCH
Carole H. Sudre
Laura Acion
Michela Antonelli
Tal Arbel
Spyridon Bakas
Arriel Benis
Matthew Blaschko
Florian Buettner
M. Jorge Cardoso
Veronika Cheplygina
Jianxu Chen
Evangelia Christodoulou
BETH A. CIMINI … (voir 58 de plus)
Gary S. Collins
Keyvan Farahani
LUCIANA FERRER
Adrian Galdran
Bram van Ginneken
Ben Glocker
PATRICK GODAU
Robert Haase
Daniel A. Hashimoto
Michael M. Hoffman
Merel Huisman
Fabian Isensee
Pierre Jannin
CHARLES E. KAHN
Dagmar Kainmueller
BERNHARD KAINZ
Alexandros Karargyris
Alan Karthikesalingam
Hannes 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
Maarten van Smeden
Ronald M. Summers
Abdel A. Taha
Aleksei Tiulpin
Sotirios A. Tsaftaris
Ben Van Calster
Gael Varoquaux
Manuel Wiesenfarth
Ziv R. Yaniv
PAUL F. JÄGER
Lena Maier-Hein
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligen… (voir plus)ce (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.
2024-01-31
Nature Methods (publié)
doi.org
arxiv.org
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Nima Fathi
Amar Kumar
Brennan Nichyporuk
Mohammad Havaei
Tal Arbel
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.
2023-12-31
MIDL (publié)
doi.org
openreview.net
HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
Daniel Duenias
Brennan Nichyporuk
Tal Arbel
Tammy Riklin Raviv
The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Recor… (voir plus)ds (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a comprehensive understanding of the clinical condition of a patient, improving diagnosis and treatment decision. Deep Neural Networks (DNNs) consistently demonstrate 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 generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex and multi-class 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. A link to our code can be found at https://github.com/daniel4725/HyperFusion
2023-12-31
arXiv (prépublication)
doi.org
arxiv.org
Current AI applications in neurology: Brain imaging
Tal Arbel
Joshua D. Durso-Finley
Jean-Pierre R. Falet
Raghav Mehta
Douglas Arnold
Nick Pawlowski
2023-11-30
Journal of the Neurological Sciences (publié)
doi.org
Debiasing Counterfactuals in the Presence of Spurious Correlations
Amar Kumar
Nima Fathi
Raghav Mehta
Brennan Nichyporuk
Jean-Pierre R. Falet
Sotirios A. Tsaftaris
Tal Arbel
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.
2023-10-08
Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging (publié)
doi.org
openreview.net
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
Joshua D. Durso-Finley
Jean-Pierre R. Falet
Raghav Mehta
Douglas Arnold
Nick Pawlowski
Tal Arbel
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.
2023-10-07
OpenReview.net/Archive (publié)
doi.org
openreview.net
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis
Changjian Shui
Justin Szeto
Raghav Mehta
Douglas L. Arnold
Tal Arbel
2023-10-07
OpenReview (publié)
doi.org
openreview.net
Grow-push-prune: Aligning deep discriminants for effective structural network compression
Qing Tian
Tal Arbel
James J. Clark
2023-05-31
Computer Vision and Image Understanding (publié)
doi.org
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
Tal Arbel
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.
2023-04-24
ArXiv (prépublication)
doi.org
arxiv.org
Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis
Raghav Mehta
Changjian Shui
Tal Arbel
2023-04-03
MIDL.io/2023/Conference (poster)
doi.org
proceedings.mlr.press
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 … (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,...).
2022-12-03
Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (publié)
doi.org
proceedings.mlr.press