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

Uncertainty Evaluation Metric for Brain Tumour Segmentation
Raghav Mehta
Angelos Filos
Yarin Gal
Tal Arbel
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in … (voir plus)the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
2020-04-16
MIDL.io/2020/Conference (accepté)
openreview.net
openreview.net
CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images
Nazanin Mohammadi Sepahvand
Douglas Arnold
Tal Arbel
Accurate detection and segmentation of new lesional activity in longitudinal Magnetic Resonance Images (MRIs) of patients with Multiple Scle… (voir plus)rosis (MS) is important for monitoring disease activity, as well as for assessing treatment effects. In this work, we present the first deep learning framework to automatically detect and segment new and enlarging (NE) T2w lesions from longitudinal brain MRIs acquired from relapsing-remitting MS (RRMS) patients. The proposed framework is an adapted 3D U-Net [1] which includes as inputs the reference multi-modal MRI and T2-weighted lesion maps, as well an attention mechanism based on the subtraction MRI (between the two timepoints) which serves to assist the network in learning to differentiate between real anatomical change and artifactual change, while constraining the search space for small lesions. Experiments on a large, proprietary, multi -center, multi-modal, clinical trial dataset consisting of 1677 multi-modal scans illustrate that network achieves high overall detection accuracy (detection AUC=.95), outperforming (1) a U-Net without an attention mechanism (de-tection AUC=.93), (2) a framework based on subtracting independent T2-weighted segmentations (detection AUC=.57), and (3) DeepMedic (detection AUC=.84) [2], particularly for small lesions. In addition, the method was able to accurately classify patients as active/inactive with (sensitivities of. 69 and specificities of. 97).
2020-04-02
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (publié)
doi.org
Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track
Tal Arbel
Ismail Ben Ayed
Marleen de Bruijne
Maxime Descoteaux
Hervé Lombaert
Chris Pal
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (M… (voir plus)IDL 2020), held in Montreal, Canada, 6-9 July 2020. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2020 Full Paper Track are published in the Proceedings of Machine Learning Research (PMLR).
2019-12-31
arXiv (prépublication)
doi.org
arxiv.org
Fear in Hebrew
Tal Arbel
2019-10-31
Historical Studies in the Natural Sciences (publié)
doi.org
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference
Raghav Mehta
Thomas Christinck
Tanya Nair
Aurélie Bussy
Paul Lemaitre
Swapna Premasiri
Manuela Costantino
Mallar Chakravarty
Douglas Arnold
Yarin Gal
Tal Arbel
2019-10-16
UNSURE/CLIP@MICCAI (publié)
doi.org
Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI
Joshua D. Durso-Finley
Douglas Arnold
Tal Arbel
2019-10-16
BrainLes@MICCAI (publié)
doi.org
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases
Barleen Kaur
Paul Lemaitre
Raghav Mehta
Nazanin Mohammadi Sepahvand
Doina Precup
Douglas Arnold
Tal Arbel
2019-10-12
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data (publié)
doi.org
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
Nils D. Forkert
Robust and reliable stroke lesion segmentation is a crucial step toward employing lesion volume as an independent endpoint for randomized tr… (voir plus)ials. The aim of this work was to develop and evaluate a novel method to segment sub-acute ischemic stroke lesions from fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) datasets. After preprocessing of the datasets, a Bayesian technique based on Gabor textures extracted from the FLAIR signal intensities is utilized to generate a first estimate of the lesion segmentation. Using this initial segmentation, a customized voxel-level Markov random field model based on intensity as well as Gabor texture features is employed to refine the stroke lesion segmentation. The proposed method was developed and evaluated based on 151 multi-center datasets from three different databases using a leave-one-patient-out validation approach. The comparison of the automatically segmented stroke lesions with manual ground truth segmentation revealed an average Dice coefficient of 0.582, which is in the upper range of previously presented lesion segmentation methods using multi-modal MRI datasets. Furthermore, the results obtained by the proposed technique are superior compared to the results obtained by two methods based on convolutional neural networks and three phase level-sets, respectively, which performed best in the ISLES 2015 challenge using multi-modal imaging datasets. The results of the quantitative evaluation suggest that the proposed method leads to robust lesion segmentation results using FLAIR MRI datasets only as a follow-up sequence.
2019-05-23
Frontiers in Neurology (publié)
doi.org
Prediction of Progression in Multiple Sclerosis Patients
Adrian Tousignant
Paul Lemaitre
Doina Precup
Douglas Arnold
Tal Arbel
We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from… (voir plus) baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66 +- 0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701 +- 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.
2019-02-27
MIDL.io/2019/Conference (accepté)
openreview.net
openreview.net
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 Landman
Keno März
Oskar Maier … (voir 18 de plus)
Klaus Maier-Hein
Bjoern Menze
Henning Müller
Peter F. Neher
Wiro Niessen
Nasir Rajpoot
Gregory C. Sharp
Korsuk Sirinukunwattana
Stefanie Speidel
Christian Stock
Danail Stoyanov
Abdel Aziz Taha
Fons van der Sommen
Ching-Wei Wang
Marc-André Weber
Guoyan Zheng
Pierre Jannin
Annette Kopp-Schneider
2019-01-29
Nature Communications (publié)
doi.org
Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data
Adrian Tousignant
Paul Lemaitre
Doina Precup
Douglas L. Arnold
Tal Arbel
2018-12-31
International Conference on Medical Imaging with Deep Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures - First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings
Raghav Mehta
Thomas Christinck
Tanya Nair
Paul Lemaitre
Douglas Arnold
Tal Arbel
2018-12-31
UNSURE/CLIP@MICCAI (publié)
doi.org