Tal Arbel

Membre Académique Associé
Tal Arbel
Professeure agrégée, McGill University
Tal Arbel

Mes objectifs de recherche sont de développer de nouveaux cadres probabilistes d’apprentissage automatique en vision par ordinateur et en imagerie médicale, particulièrement dans le contexte de la neurologie et de la neurochirurgie. Les travaux récents se sont concentrés sur le développement de modèles graphiques probabilistes pour la détection et la segmentation de pathologies (lésions, tumeurs) dans de grands ensembles de données multicentriques d’images de patients, sur l’identification automatique de biomarqueurs d’imagerie qui prédisent la progression de la maladie chez les patients ainsi que les répondants potentiels aux traitements. J’ai beaucoup travaillé sur le développement de techniques d’enregistrement d’images multimodales rapides et efficaces pour des interventions cliniques, comme la neurochirurgie guidée par l’image. Des recherches récentes ont mené à l’élaboration de nouveaux cadres de travail pour apprendre comment les plis corticaux à la surface du cerveau varient d’une population à l’autre.

Publications

2020-09

Deep discriminant analysis for task-dependent compact network search
Qing Tian, Tal Arbel and James J. Clark
arXiv preprint arXiv:2009.13716
(2020-09-29)
dblp.uni-trier.dePDF

2020-08

Biomedical Image Analysis Challenges (BIAS) Reporting Guideline
Lena Maier-Hein, Annika Reinke, Michal Kozubek, Anne L. Martel, Tal Arbel, Matthias Eisenmann, Allan Hanbury, Pierre Jannin, Henning Müller, Sinan Onogur, Julio Saez-Rodriguez, Bram van Ginneken, Annette Kopp-Schneider and Bennett A. Landman
(venue unknown)
(2020-08-31)
doi.orgPDF
BIAS: Transparent reporting of biomedical image analysis challenges
Lena Maier-Hein, Annika Reinke, Michal Kozubek, Anne L. Martel, Tal Arbel, Matthias Eisenmann, Allan Hanbury, Pierre Jannin, Henning Müller, Sinan Onogur, Julio Saez-Rodriguez, Bram van Ginneken, Annette Kopp-Schneider and Bennett A. Landman
Medical Image Analysis
(2020-08-21)
www.sciencedirect.com

2020-06

Medical Imaging with Deep Learning: MIDL 2020 - Short Paper Track.
Tal Arbel, Ismail Ben Ayed, Marleen de Bruijne, Maxime Descoteaux, Herve Lombaert and Chris Pal
arXiv preprint arXiv:2007.02319
(2020-06-29)
ui.adsabs.harvard.eduPDF

2020-05

Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.
Aaron Carass, Snehashis Roy, Adrian Gherman, Jacob C. Reinhold, Andrew Jesson, Tal Arbel, Oskar Maier, Heinz Handels, Mohsen Ghafoorian, Bram Platel, Ariel Birenbaum, Hayit Greenspan, Dzung L. Pham, Ciprian M. Crainiceanu, Peter A. Calabresi, Jerry L. Prince, William R. Gray Roncal, Russell T. Shinohara and Ipek Oguz
Scientific Reports
(2020-05-19)
www.nature.com

2020-04

CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images
Nazanin Mohammadi Sepahvand, Douglas L. Arnold and Tal Arbel
ISBI 2020
(2020-04-03)
doi.org

2020-01

Uncertainty Evaluation Metric for Brain Tumour Segmentation.
Raghav Mehta, Angelos Filos, Yarin Gal and Tal Arbel
Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation.
Tanya Nair, Doina Precup, Douglas L. Arnold and Tal Arbel

2019-12

Author correction: Why rankings of biomedical image analysis competitions should be interpreted with care (Nature Communications, (2018), 9, 1, (5217), 10.1038/s41467-018-07619-7)
Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier... (18 more)
Nature Communications
(2019-12-01)
research.tue.nl

2019-10

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference
Raghav Mehta, Thomas Christinck, Tanya Nair, Paul Lemaître, Douglas L. Arnold and Tal Arbel
UNSURE/CLIP@MICCAI
(2019-10-17)
doi.org
Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI
Joshua Durso-Finley, Douglas L. Arnold and Tal Arbel
International MICCAI Brainlesion Workshop
(2019-10-17)
doi.org
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases
Barleen Kaur, Paul Lemaître, Raghav Mehta, Nazanin Mohammadi Sepahvand, Doina Precup, Douglas L. Arnold and Tal Arbel
DART/MIL3ID@MICCAI
(2019-10-13)
link.springer.com
BIAS: Transparent reporting of biomedical image analysis challenges
Lena Maier-Hein, Annika Reinke, Michal Kozubek, Anne L. Martel, Tal Arbel, Matthias Eisenmann, Allan Hanbuary, Pierre Jannin, Henning Müller, Sinan Onogur, Julio Saez-Rodriguez, Bram van Ginneken, Annette Kopp-Schneider and Bennett Landman
arXiv preprint arXiv:1910.04071
(2019-10-09)
ui.adsabs.harvard.eduPDF

2019-05

Stroke Lesion Segmentation in FLAIR MRI Datasets Using Customized Markov Random Fields.
Nagesh K. Subbanna, Deepthi Rajashekar, Bastian Cheng, Götz Thomalla, Jens Fiehler, Tal Arbel and Nils D. Forkert
Frontiers in Neurology
(2019-05-24)
www.frontiersin.org
Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data
Adrian Tousignant, Paul Lemaître, Doina Precup, Douglas L. Arnold and Tal Arbel
International Conference on Medical Imaging with Deep Learning
(2019-05-24)
proceedings.mlr.pressPDF

2019-01

Why rankings of biomedical image analysis competitions should be interpreted with care (vol 9, 5217, 2018)
Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno Maerz, Oskar Maier... (18 more)
Nature Communications
(2019-01-30)
eprints.whiterose.ac.uk
Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care.
Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier... (18 more)
Nature Communications
(2019-01-30)
www.nature.com

2018-12

Prediction of Progression in Multiple Sclerosis Patients
Adrian Tousignant, Paul Lemaître, Doina Precup, Douglas Arnold and Tal Arbel
International Conference on Medical Imaging with Deep Learning -- Full Paper Track
(2018-12-13)
openreview.netPDF
Why rankings of biomedical image analysis competitions should be interpreted with care
Lena Maier-Hein, Matthias Eisenmann, Annika Reinke, Sinan Onogur, Marko Stankovic, Patrick Scholz, Tal Arbel, Hrvoje Bogunovic, Andrew P. Bradley, Aaron Carass, Carolin Feldmann, Alejandro F. Frangi, Peter M. Full, Bram van Ginneken, Allan Hanbury, Katrin Honauer, Michal Kozubek, Bennett A. Landman, Keno März, Oskar Maier... (18 more)
Nature Communications
(2018-12-06)
www.nature.comPDF

2018-11

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler... (403 more)
arXiv preprint arXiv:1811.02629
(2018-11-05)
hal.inria.frPDF

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