Tal Arbel

Mila > À propos de Mila > Équipe > Tal Arbel
Membre Académique Associé
Tal Arbel
Professeur, McGill University
Tal Arbel

Mes objectifs de recherche portent sur le développement de nouveaux cadres d’apprentissage automatique probabilistes pour la vision par ordinateur et l’imagerie médicale dans les domaines de la neurologie et de la neurochirurgie. Mes projets actuels sont axés sur le développement de modèles graphiques probabilistes pour la détection et la segmentation des pathologies (lésions, tumeurs) dans de grands ensembles d’images de patients multicentriques, ainsi que la modélisation et la transmission des incertitudes dans les modèles d’apprentissage profond. J’ai longuement travaillé sur la mise au point de techniques d’enregistrement d’images multimodales rapides et efficaces pour les interventions cliniques, telles que la neurochirurgie guidée par l’image. De plus, je travaille sur l’identification automatique des biomarqueurs d’imagerie pour prévoir la progression de la maladie chez les patients atteints de sclérose en plaques, ainsi que sur l’identification des répondeurs potentiels au traitement. À cela s’ajoute le développement de modèles spatio-temporels afin de suivre la progression de la maladie, intégrant des informations cliniques et d’imagerie dans l’apprentissage et l’inférence.

Publications

2021-04

Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Matthias Eisenmann, Minu Dietlinde Tizabi, Carole H. Sudre, Tim Rädsch, Michela Antonelli, Tal Arbel, Spyridon Bakas, M. Jorge Cardoso, Veronika Cheplygina, Keyvan Farahani, Ben Glocker, Doreen Heckmann-Nötzel, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Jens Kleesiek, Tahsin M. Kurç, Michal Kozubek, Bennett A. Landman... (14 more)
arXiv preprint arXiv:2104.05642
(2021-04-12)
ui.adsabs.harvard.eduPDF
Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting
Brennan Nichyporuk, Justin Szeto, Douglas Arnold and Tal Arbel
(venue unknown)
(2021-04-09)
openreview.netPDF

2021-03

Accounting for Variance in Machine Learning Benchmarks
Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux and Pascal Vincent
Fourth Conference on Machine Learning and Systems ( MLsys 2021)
(2021-03-15)
proceedings.mlsys.orgPDF[Also on arXiv preprint arXiv:2103.03098 (2021-03-01)]

2021-02

HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images
Saverio Vadacchino, Raghav Mehta, Nazanin Mohammadi Sepahvand, Brennan Nichyporuk, James J. Clark and Tal Arbel
Medical Imaging with Deep Learning
(2021-02-09)
dblp.uni-trier.dePDF
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images
Saverio Vadacchino, Raghav Mehta, Nazanin Mohammadi Sepahvand, Brennan Nichyporuk, James J. Clark and Tal Arbel
arXiv preprint arXiv:2103.16617
(2021-02-09)
dblp.uni-trier.dePDF
Task dependent deep LDA pruning of neural networks
Qing Tian, Tal Arbel and James J. Clark

2020-10

Optimization over Random and Gradient Probabilistic Pixel Sampling for Fast, Robust Multi-Resolution Image Registration
Boris N. Oreshkin and Tal Arbel
arXiv preprint arXiv:2010.02505
(2020-10-02)
ui.adsabs.harvard.eduPDF
Uncertainty driven probabilistic voxel selection for image registration
Boris N. Oreshkin and Tal Arbel
arXiv preprint arXiv:2010.00988
(2020-10-02)
ui.adsabs.harvard.eduPDF

2020-09

Grow-Push-Prune: aligning deep discriminants for effective structural network compression
Qing Tian, Tal Arbel and James J. Clark
arXiv: Computer Vision and Pattern Recognition
(2020-09-29)
ui.adsabs.harvard.eduPDF
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.org
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)
europepmc.org

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)
ieeexplore.ieee.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)
link.springer.com
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)
link.springer.com
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)
export.arxiv.orgPDF

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)
europepmc.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)
europepmc.org

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)

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