Tal Arbel

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

Berardino Barile

Postdoctorat - McGill

Joshua Durso-finley

Doctorat - McGill

Nima Fathi

Maîtrise recherche - McGill

Gian Favero

Maîtrise recherche - McGill

elizabeth.janes@mila.quebec

Ethan Honey

Doctorat - McGill

ethan.honey@mila.quebec

Elizabeth Laura Janes

Maîtrise recherche - McGill

Emily Kaczmarek

Doctorat - McGill

emily.kaczmarek@mail.mcgill.ca

Amar Kumar

Doctorat - McGill

Mohamed Mohamed

Maîtrise recherche - McGill

Parham Saremi

Maîtrise recherche - McGill

Xing Shen

Maîtrise recherche - McGill

Maîtrise recherche - McGill

Zahra TehraniNasab

Maîtrise recherche - McGill

Mona Wang Wang

Baccalauréat - McGill

monaw7777@gmail.com

Ritchie Yu

Baccalauréat - McGill

Publications

CNN Prediction of Future Disease Activity for Multiple Sclerosis Patients from Baseline MRI and Lesion Labels

Nazanin Mohammadi Sepahvand

Tal Hassner

Douglas Arnold

2018-09-16

BrainLes@MICCAI (publié)

3D U-Net for Brain Tumour Segmentation

Raghav Mehta

2018-09-16

BrainLes@MICCAI (publié)

How to Exploit Weaknesses in Biomedical Challenge Design and Organization

Annika Reinke

Matthias Eisenmann

Sinan Onogur

Marko Stankovic

Patrick Scholz

Peter M. Full

Hrvoje Bogunovic

Bennett Landman

Oskar Maier

Bjoern Menze

Gregory C. Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

F. V. D. Sommen

Guoyan Zheng

Henning Müller

Michal Kozubek

Andrew P. Bradley

Pierre Jannin … (voir 2 de plus)

Annette Kopp-Schneider

Lena Maier-Hein

2018-09-13

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (publié)

RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours

Raghav Mehta

2018-09-12

Simulation and Synthesis in Medical Imaging (publié)

arxiv.org

Structured deep Fisher pruning for efficient facial trait classification

Qing Tian

James J. Clark

2018-09-01

Image and Vision Computing (publié)

Fisher Pruning of Deep Nets for Facial Trait Classification

Qing Tian

James J. Clark

Although deep nets have resulted in high accuracies for various visual tasks, their computational and space requirements are prohibitively h… (voir plus)igh for inclusion on devices without high-end GPUs. In this paper, we introduce a neuron/filter level pruning framework based on Fisher's LDA which leads to high accuracies for a wide array of facial trait classification tasks, while significantly reducing space/computational complexities. The approach is general and can be applied to convolutional, fully-connected, and module-based deep structures, in all cases leveraging the high decorrelation of neuron activations found in the pre-decision layer and cross-layer deconv dependency. Experimental results on binary and multi-category facial traits from the LFWA and Adience datasets illustrate the framework's comparable/better performance to state-of-the-art pruning approaches and compact structures (e.g. SqueezeNet, MobileNet). Ours successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG-16, 82% for GoogLeNet) and with significant FLOP reductions (83% for VGG-16, 64% for GoogLeNet).

2018-03-21

ArXiv (prépublication)

arxiv.org

Task-specific Deep LDA pruning of neural networks

Qing Tian

James J. Clark

With deep learning's success, a limited number of popular deep nets have been widely adopted for various vision tasks. However, this usually… (voir plus) results in unnecessarily high complexities and possibly many features of low task utility. In this paper, we address this problem by introducing a task-dependent deep pruning framework based on Fisher's Linear Discriminant Analysis (LDA). The approach can be applied to convolutional, fully-connected, and module-based deep network structures, in all cases leveraging the high decorrelation of neuron motifs found in the pre-decision layer and cross-layer deconv dependency. Moreover, we examine our approach's potential in network architecture search for specific tasks and analyze the influence of our pruning on model robustness to noises and adversarial attacks. Experimental results on datasets of generic objects, as well as domain specific tasks (CIFAR100, Adience, and LFWA) illustrate our framework's superior performance over state-of-the-art pruning approaches and fixed compact nets (e.g. SqueezeNet, MobileNet). The proposed method successfully maintains comparable accuracies even after discarding most parameters (98%-99% for VGG16, up to 82% for the already compact InceptionNet) and with significant FLOP reductions (83% for VGG16, up to 64% for InceptionNet). Through pruning, we can also derive smaller, but more accurate and more robust models suitable for the task.

Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight cases

Ian J. Gerard

Marta Kersten-Oertel

Simon Drouin

Jeffery A. Hall

Kevin Petrecca

Dante De Nigris

Daniel A. Di Giovanni

D. Louis Collins

Abstract. We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented … (voir plus)reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient’s cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.

2018-01-26

Journal of Medical Imaging (publié)

Challenging Conventional Segmentation Evaluation Metrics Focal Pathology ( Lesion and Tumour ) Segmentation from Patient Images

How can we do better ? Pitfalls in biomedical challenge design and how to address them

Annika Reinke

Matthias Eisenmann

Sinan Onogur

Marko Stankovic

Patrick Scholz

Hrvoje Bogunovic

Andrew P. Bradley

Aaron

Carass

Carolin Feldmann

Alejandro F. Frangi

Peter M. Full

Bram Ginneken Van

Ginneken

Allan Hanbury

Katrin Honauer

Michal Kozubek

Adam Bennett

Landman … (voir 22 de plus)

Keno März

Oskar Maier

Klaus Maier-Hein

Bjoern Menze

Henning Müller

Peter F. Neher

Wiro Niessen

NASIR RAJPOOT

Catherine Gregory

Sharp

Korsuk Sirinukunwattana

Stefanie Speidel

Christian Stock

Danail

Stoyanov

Abdel Aziz Taha

F. V. D. Sommen

Ching-Wei Wang

Marc-André Weber

Guoyan Zheng

Pierre Jannin

Lena Maier-Hein

Since the first MICCAI grand challenge was organized in 2007 [1], the impact of biomedical image analysis challenges on both the research fi… (voir plus)eld as well as on individual careers has been steadily growing. For example, the acceptance of a journal article today often depends on the performance of a new algorithm being assessed against the state-ofthe-art work on publicly available challenge datasets. Furthermore, the results are also important for the individuals scientific careers as well as the potential that algorithms can be translated into clinical practice. Yet, while the publication of papers in scientific journals and prestigious conferences, such as MICCAI, undergoes strict quality control, the design and organization of challenges do not. To investigate the effect of common practice, we have formed an international initiative dedicated to analyzing and improving a variety of aspects related to biomedical challenge design, execution and reporting [2]. In the first part of our abstract presentation at LABELS workshop, we are going to present some of the major pitfalls related to biomedical image analysis challenges today. Specifically, we will look at the following research questions: RQ1: How robust are challenge rankings? What is the effect of – the specific test cases used? – the specific metric variant(s) applied? – the rank aggregation method chosen (e.g. aggregation of metric values with the mean vs median)? ? Shared first/senior authors.

Predicting Future Disease Activity and Treatment Responders for Multiple Sclerosis Patients Using a Bag-of-Lesions Brain Representation

Andrew Doyle

Doina Precup

Douglas Arnold

2017-09-04

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 (publié)

Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics

M. Cardoso

Enzo Ferrante

Xavier Pennec

Adrian Dalca

Sarah Parisot

S. Joshi

Nematollah Batmanghelich

Aristeidis Sotiras

Mads Lenstrup Nielsen

M. Sabuncu

Tom Fletcher

Li Shen

Stanley Durrleman

Stefan H. Sommer

2017-09-01

Lecture Notes in Computer Science (publié)