Faculty profile

Tal Arbel

ResearcherElectrical and Computer EngineeringMcGill University

This profile is built from public research funding records (CIHR, NSERC and SSHRC) and PubMed. We have not imported them from a McGill University directory, so their courses and email address may be missing. Find their university profile.

Tal Arbel

Researcher

Electrical and Computer Engineering

Engineering

McGill University

Montreal, Quebec, Canada

Supervision signals

Has led research grants, which requires an independent research appointment. Ask their department whether they supervise graduate students.
7 publications in the last five years, latest in 2025.
Senior (last) author on 1 recent paper, a sign of leading a research group.
Principal investigator on 8 research grants (8 NSERC), most recently in 2023.

These signals come from public records, including CIHR, NSERC and SSHRC funding decisions. Ask the professor or the graduate program whether they are taking new students.

Academic metrics

Publications

404

Citations

Collaborators

Grants

Publications by year

Counts cover the publications linked to this profile. Citations and the h-index appear when the source records them.

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Signals and details

Research

Bayesian deep learning causal inference computer vision deep representation learning domain adaptation explainable deep learning models image-based precision medicine medical image analysis

Latest papers

Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation.
Advances in neural information processing systems · 2025
Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.
Medical image analysis · 2025
The role of AI for MRI-analysis in multiple sclerosis-A brief overview.
Frontiers in artificial intelligence · 2025

All 34 publications

Latest funding

Causal Representation Learning for Trustworthy Temporal Medical Image Analysis
NSERC · 2023 · Principal investigator
$138,000
Automatic segmentation of healthy tissues and tumours in patient brain images using 3D fully convolutional neural networks
NSERC · 2016 · Principal investigator
$112,000
Probabilistic Inference in Computer Vision and Medical Imaging
NSERC · 2015 · Principal investigator
$301,000

All 13 grants and awards

34 publications.

Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation.

Trang B, Saremi P, Wang AQ, Huang F, TehraniNasab Z, Kumar A, Arbel T, Fei-Fei L, Adeli E

Advances in neural information processing systems2025

PMID: 42088644

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

Duenias D, Nichyporuk B, Arbel T, Riklin Raviv T, ADNI

Medical image analysis2025

DOI: 10.1016/j.media.2025.103503 PMID: 40037055

The role of AI for MRI-analysis in multiple sclerosis-A brief overview.

Falet JR, Nobile S, Szpindel A, Barile B, Kumar A, Durso-Finley J, Arbel T, Arnold DL

Frontiers in artificial intelligence2025

DOI: 10.3389/frai.2025.1478068 PMID: 40265105

Metrics reloaded: recommendations for image analysis validation.

Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M, Reyes M, Riegler MA, Wiesenfarth M, Kavur AE, Sudre CH, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Rädsch T, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko MB, Cardoso MJ, Cheplygina V, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Karthikesalingam A, Kofler F, Kopp-Schneider A, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Mattson P, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rajpoot N, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, van Smeden M, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Jäger PF

Nature methods2024

DOI: 10.1038/s41592-023-02151-z PMID: 38347141

Understanding metric-related pitfalls in image analysis validation.

Reinke A, Tizabi MD, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Kavur AE, Rädsch T, Sudre CH, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Buettner F, Cardoso MJ, Cheplygina V, Chen J, Christodoulou E, Cimini BA, Farahani K, Ferrer L, Galdran A, van Ginneken B, Glocker B, Godau P, Hashimoto DA, Hoffman MM, Huisman M, Isensee F, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Kleesiek J, Kofler F, Kooi T, Kopp-Schneider A, Kozubek M, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rafelski SM, Rajpoot N, Reyes M, Riegler MA, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Yaniv ZR, Jäger PF, Maier-Hein L

Nature methods2024

DOI: 10.1038/s41592-023-02150-0 PMID: 38347140

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

Falet JR, Durso-Finley J, Nichyporuk B, Schroeter J, Bovis F, Sormani MP, Precup D, Arbel T, Arnold DL

Nature communications2022

DOI: 10.1038/s41467-022-33269-x PMID: 36163349

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

Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, Dätwyler K, Meier R, Radojewski P, Murugesan GK, Nalawade S, Ganesh C, Wagner B, Yu FF, Fei B, Madhuranthakam AJ, Maldjian JA, Daza L, Gómez C, Arbeláez P, Dai C, Wang S, Reynaud H, Mo Y, Angelini E, Guo Y, Bai W, Banerjee S, Pei L, Ak M, Rosas-González S, Zemmoura I, Tauber C, Vu MH, Nyholm T, Löfstedt T, Ballestar LM, Vilaplana V, McHugh H, Maso Talou G, Wang A, Patel J, Chang K, Hoebel K, Gidwani M, Arun N, Gupta S, Aggarwal M, Singh P, Gerstner ER, Kalpathy-Cramer J, Boutry N, Huard A, Vidyaratne L, Rahman MM, Iftekharuddin KM, Chazalon J, Puybareau E, Tochon G, Ma J, Cabezas M, Llado X, Oliver A, Valencia L, Valverde S, Amian M, Soltaninejad M, Myronenko A, Hatamizadeh A, Feng X, Dou Q, Tustison N, Meyer C, Shah NA, Talbar S, Weber MA, Mahajan A, Jakab A, Wiest R, Fathallah-Shaykh HM, Nazeri A, Milchenko M, Marcus D, Kotrotsou A, Colen R, Freymann J, Kirby J, Davatzikos C, Menze B, Bakas S, Gal Y, Arbel T

The journal of machine learning for biomedical imaging2022Senior author

DOI: 10.1007/978-3-030-46640-4\_21 PMID: 36998700

BIAS: Transparent reporting of biomedical image analysis challenges.

Maier-Hein L, Reinke A, Kozubek M, Martel AL, Arbel T, Eisenmann M, Hanbury A, Jannin P, Müller H, Onogur S, Saez-Rodriguez J, van Ginneken B, Kopp-Schneider A, Landman BA

Medical image analysis2020

DOI: 10.1016/j.media.2020.101796 PMID: 32911207

Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.

Carass A, Roy S, Gherman A, Reinhold JC, Jesson A, Arbel T, Maier O, Handels H, Ghafoorian M, Platel B, Birenbaum A, Greenspan H, Pham DL, Crainiceanu CM, Calabresi PA, Prince JL, Roncal WRG, Shinohara RT, Oguz I

Scientific reports2020

DOI: 10.1038/s41598-020-64803-w PMID: 32427874

Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

Nair T, Precup D, Arnold DL, Arbel T

Medical image analysis2020Senior author

DOI: 10.1016/j.media.2019.101557 PMID: 31677438

Causal Representation Learning for Trustworthy Temporal Medical Image Analysis

Principal investigatorNSERC2023Discovery Grants Program - Individual

Principal investigators: Arbel, Tal

Keywords: computer vision; medical image analysis; uncertainty estimation; bayesian deep learning; deep representation learning; explainable deep learning models; domain adaptation; causal inference; temporal models; image-based precision medicine

$138,000

Automatic segmentation of healthy tissues and tumours in patient brain images using 3D fully convolutional neural networks

Principal investigatorNSERC2016Collaborative Research and Development Grants

Principal investigators: Arbel, Tal

$112,000

Probabilistic Inference in Computer Vision and Medical Imaging

Principal investigatorNSERC2015Discovery Grants Program - Individual

Principal investigators: Arbel, Tal

$301,000

NSERC CREATE Program in Medical Image Analysis

Co-investigatorNSERC2012Collaborative Research and Training Experience

Principal investigators: Siddiqi, Kaleem

$1,650,000

Probabilistic segmentation of multiple sclerosis lesions brain images

Principal investigatorNSERC2010Collaborative Research and Development Grants

Principal investigators: Arbel, Tal

$173,752

Probabilistic inference in computer vision and medical imaging

Principal investigatorNSERC2010Discovery Grants Program - Accelerator Supplements

Principal investigators: Arbel, Tal

$120,000

Probabilistic inference in computer vision and medical imaging

Principal investigatorNSERC2010Discovery Grants Program - Individual

Principal investigators: Arbel, Tal

$215,000

Computational and statistical tools for image-guided neurosurgery of brain tumours

Co-investigatorNSERC2010Collaborative Health Research Projects

Principal investigators: Collins, DLouis

$314,099

Tissue segmentation in MRI

Co-investigatorNSERC2007Strategic Projects - Group

Principal investigators: Collins, Louis

$615,266

3-D Visualization and gestural interaction with multimodal neurological data

Co-investigatorNSERC2007Strategic Projects Supplemental Competition

Principal investigators: Cooperstock, Jeremy

$196,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

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Tal Arbel

Research

Latest papers

Latest funding

Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation.

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

The role of AI for MRI-analysis in multiple sclerosis-A brief overview.

Metrics reloaded: recommendations for image analysis validation.

Understanding metric-related pitfalls in image analysis validation.

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

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

BIAS: Transparent reporting of biomedical image analysis challenges.

Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.

Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

Causal Representation Learning for Trustworthy Temporal Medical Image Analysis

Automatic segmentation of healthy tissues and tumours in patient brain images using 3D fully convolutional neural networks

Probabilistic Inference in Computer Vision and Medical Imaging

NSERC CREATE Program in Medical Image Analysis

Probabilistic segmentation of multiple sclerosis lesions brain images

Probabilistic inference in computer vision and medical imaging

Probabilistic inference in computer vision and medical imaging

Computational and statistical tools for image-guided neurosurgery of brain tumours

Tissue segmentation in MRI

3-D Visualization and gestural interaction with multimodal neurological data

Frequent collaborators

Doina Precup

Simon Drouin

Kevin Petrecca

Yiming Xiao

Matthew Toews

Hassan Rivaz

Luciana Catanese

Catherine Laporte

Tal Arbel

Research

Latest papers

Latest funding

Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation.

Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.

The role of AI for MRI-analysis in multiple sclerosis-A brief overview.

Metrics reloaded: recommendations for image analysis validation.

Understanding metric-related pitfalls in image analysis validation.

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

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

BIAS: Transparent reporting of biomedical image analysis challenges.

Evaluating White Matter Lesion Segmentations with Refined S&#xf8;rensen-Dice Analysis.

Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.

Causal Representation Learning for Trustworthy Temporal Medical Image Analysis

Automatic segmentation of healthy tissues and tumours in patient brain images using 3D fully convolutional neural networks

Probabilistic Inference in Computer Vision and Medical Imaging

NSERC CREATE Program in Medical Image Analysis

Probabilistic segmentation of multiple sclerosis lesions brain images

Probabilistic inference in computer vision and medical imaging

Probabilistic inference in computer vision and medical imaging

Computational and statistical tools for image-guided neurosurgery of brain tumours

Tissue segmentation in MRI

3-D Visualization and gestural interaction with multimodal neurological data

Frequent collaborators

Doina Precup

Simon Drouin

Kevin Petrecca

Yiming Xiao

Matthew Toews

Hassan Rivaz

Luciana Catanese

Catherine Laporte

Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.