Tal Arbel

Biography

Tal Arbel is a professor in the Department of Electrical and Computer Engineering at McGill University, where she is the director of the Probabilistic Vision Group and Medical Imaging Lab in the Centre for Intelligent Machines.

She is also a Canada CIFAR AI Chair, an associate academic member of Mila – Quebec Artificial Intelligence Institute and an associate member of the Goodman Cancer Research Centre.

Arbel’s research focuses on the development of probabilistic deep learning methods in computer vision and medical image analysis for a wide range of real-world applications, with a focus on neurological diseases.

She is a recipient of the 2019 McGill Engineering Christophe Pierre Research Award and regularly serves on organizing committees for major international conferences in computer vision and medical image analysis, including for the Medical Image Computing and Computer-Assisted Intervention Society/MICCAI, the Medical Imaging with Deep Learning/MIDL, the International Conference on Computer Vision/ICCV or the Computer Vision and Pattern Recognition Conference/CVPR). She co-founded the arXiv overlay journal, Machine Learning for Biomedical Imaging (MELBA) and is currently its editor-in-chief.

Current Students

Berardino Barile

Postdoctorate - McGill University

Joshua Durso-finley

PhD - McGill University

Nima Fathi

Master's Research - McGill University

Gian Favero

Master's Research - McGill University

elizabeth.janes@mila.quebec

Ethan Honey

PhD - McGill University

ethan.honey@mila.quebec

Elizabeth Laura Janes

Master's Research - McGill University

Emily Kaczmarek

PhD - McGill University

emily.kaczmarek@mail.mcgill.ca

Anita Kriz

Master's Research - McGill University

Amar Kumar

PhD - McGill University

Mohamed Mohamed

Master's Research - McGill University

Parham Saremi

Master's Research - McGill University

Xing Shen

Master's Research - McGill University

Master's Research - McGill University

Mona Wang Wang

Undergraduate - McGill University

monaw7777@gmail.com

Ritchie Yu

Undergraduate - McGill University

Publications

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

Chris Pal

Gael Varoquaux

Pascal Vincent

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the l… (see more)earning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.

2021-01-01

MLSys (published)

arxiv.org

Deep LDA-Pruned Nets for Efﬁcient Facial Gender Classiﬁcation

Qing Tian

James J. Clark

Many real-time tasks, such as human-computer interac-tion, require fast and efﬁcient facial gender classiﬁcation. Although deep CNN nets… (see more) have been very effective for a mul-titude of classiﬁcation tasks, their high space and time de-mands make them impractical for personal computers and mobile devices without a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural network which boosts efﬁciency while maintaining high accuracy. Our net is pruned from the VGG-16 model [35] starting from the last convolutional (conv) layer where we ﬁnd neuron activations are highly uncorrelated given the gender. Through Fisher’s Linear Discriminant Analysis (LDA) [8], we show that this high decorrelation makes it safe to discard directly last conv layer neurons with high within-class variance and low between-class variance. Combined with either Support Vector Machines (SVM) or Bayesian classiﬁcation, the reduced CNNs are capable of achieving comparable (or even higher) accuracies on the LFW and CelebA datasets than the original net with fully connected layers. On LFW, only four Conv5 3 neurons are able to maintain a comparably high recognition accuracy, which results in a reduction of total network size by a factor of 70X with a 11 fold speedup. Comparisons with a state-of-the-art pruning method [12] (as well as two smaller nets [20, 24]) in terms of accuracy loss and convolutional layers pruning rate are also provided.

2021-01-01

(published)

www.semanticscholar.org

Deep discriminant analysis for task-dependent compact network search

Qing Tian

James J. Clark

Most of today's popular deep architectures are hand-engineered for general purpose applications. However, this design procedure usually lead… (see more)s to massive redundant, useless, or even harmful features for specific tasks. Such unnecessarily high complexities render deep nets impractical for many real-world applications, especially those without powerful GPU support. In this paper, we attempt to derive task-dependent compact models from a deep discriminant analysis perspective. We propose an iterative and proactive approach for classification tasks which alternates between (1) a pushing step, with an objective to simultaneously maximize class separation, penalize co-variances, and push deep discriminants into alignment with a compact set of neurons, and (2) a pruning step, which discards less useful or even interfering neurons. Deconvolution is adopted to reverse `unimportant' filters' effects and recover useful contributing sources. A simple network growing strategy based on the basic Inception module is proposed for challenging tasks requiring larger capacity than what the base net can offer. Experiments on the MNIST, CIFAR10, and ImageNet datasets demonstrate our approach's efficacy. On ImageNet, by pushing and pruning our grown Inception-88 model, we achieve better-performing models than smaller deep Inception nets grown, residual nets, and famous compact nets at similar sizes. We also show that our grown deep Inception nets (without hard-coded dimension alignment) can beat residual nets of similar complexities.

2020-09-29

arXiv.org (preprint)

dblp.uni-trier.de

BIAS: Transparent reporting of biomedical image analysis challenges

Lena Maier-Hein

Annika Reinke

Michal Kozubek

Anne L. Martel

Matthias Eisenmann

Allan Hanbury

Pierre Jannin

Henning Müller

Sinan Onogur

Julio Saez-Rodriguez

Bram van Ginneken

Annette Kopp-Schneider

Bennett Landman

2020-08-21

Medical Image Analysis (published)

arxiv.org

Medical Imaging with Deep Learning: MIDL 2020 - Short Paper Track

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… (see more)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).

2020-06-29

ArXiv (preprint)

arxiv.org

Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis

Aaron Carass

Snehashis Roy

Adrian Gherman

Jacob C. Reinhold

Andrew Jesson

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

Ipek Oguz

2020-05-19

Scientific Reports (published)

Uncertainty Evaluation Metric for Brain Tumour Segmentation

Raghav Mehta

Angelos Filos

Yarin Gal

In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in … (see more)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.

2020-04-17

MIDL.io/2020/Conference (published)

openreview.net

CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images

Nazanin Mohammadi Sepahvand

Douglas Arnold

Accurate detection and segmentation of new lesional activity in longitudinal Magnetic Resonance Images (MRIs) of patients with Multiple Scle… (see more)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).

2020-04-03

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (published)

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

Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology pr… (see more)esents several challenges to common models. These challenges impede the integration of deep learning models into real clinical workflows, where the customary process of cascading deterministic outputs from a sequence of image-based inference steps (e.g. registration, segmentation) generally leads to an accumulation of errors that impacts the accuracy of downstream inference tasks. In this paper, we propose that by embedding uncertainty estimates across cascaded inference tasks, performance on the downstream inference tasks should be improved. We demonstrate the effectiveness of the proposed approach in three different clinical contexts: (i) We demonstrate that by propagating T2 weighted lesion segmentation results and their associated uncertainties, subsequent T2 lesion detection performance is improved when evaluated on a proprietary large-scale, multi-site, clinical trial dataset acquired from patients with Multiple Sclerosis. (ii) We show an improvement in brain tumour segmentation performance when the uncertainty map associated with a synthesised missing MR volume is provided as an additional input to a follow-up brain tumour segmentation network, when evaluated on the publicly available BraTS-2018 dataset. (iii) We show that by propagating uncertainties from a voxel-level hippocampus segmentation task, the subsequent regression of the Alzheimer’s disease clinical score is improved.

2019-10-17

UNSURE/CLIP@MICCAI (published)

Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI

Joshua D. Durso-Finley

Douglas Arnold

2019-10-17

BrainLes@MICCAI (published)

Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

Barleen Kaur

Paul Lemaitre

Raghav Mehta

Nazanin Mohammadi Sepahvand

Doina Precup

Douglas Arnold

2019-10-13

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data (published)

Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data

Adrian Tousignant

Paul Lemaitre

Doina Precup

Douglas Arnold