Tal Arbel

cornelius.crijnen@mila.quebec

Cornelius Crijnen

Doctorat - McGill

Site web

oscar.cruzhernandez@mila.quebec

Oscar Cruzhernandez

Stagiaire de recherche - McGill

Charbel El Feghali

Stagiaire de recherche - McGill

charbel.elfeghali@mila.quebec

Carlotta Hoelzle

Stagiaire de recherche - McGill

carlotta.hoelzle@mila.quebec

elizabeth.janes@mila.quebec

Elizabeth Laura Janes

Maîtrise recherche - McGill

Emily Kaczmarek

Doctorat - McGill

emily.kaczmarek@mail.mcgill.ca

Google Scholar

Yusong Li

Collaborateur·rice de recherche - McGill University

yusong.li@mila.quebec

Toky Raharison Ralambomihanta

Google Scholar

Yik Yu Ng

Stagiaire de recherche - McGill

yik.ng@mila.quebec

Stagiaire de recherche - McGill

tokiniaina.raharisonralambomihan@mail.mcgill.ca

Ryan Rezai

Maîtrise recherche - McGill

ryan.rezai@mila.quebec

Rachel Ruddy

Stagiaire de recherche - McGill

rachel.ruddy@mila.quebec

PRISM : Un modèle d'IA générative explicable pour l'imagerie médicale

Minh To

Collaborateur·rice de recherche - UBC

Billets de blogue

Image of an Xray and the DDIM process to generate counterfactual version of Xrays

1 juillet 2025

par

Amar Kumar

Anita Kriz

Mohammed Havaei

Tal Arbel

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Publications

Segmentation-Consistent Probabilistic Lesion Counting

Julien Schroeter

Chelsea Myers-colet

Douglas Arnold

Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical ima… (voir plus)ging is often overlooked in favor of segmentation. This work introduces a novel continuously differentiable function that maps lesion segmentation predictions to lesion count probability distributions in a consistent manner. The proposed end-to-end approach—which consists of voxel clustering, lesion-level voxel probability aggregation, and Poisson-binomial counting—is non-parametric and thus offers a robust and consistent way to augment lesion segmentation models with post hoc counting capabilities. Experiments on Gadolinium-enhancing lesion counting demonstrate that our method outputs accurate and well-calibrated count distributions that capture meaningful uncertainty information. They also reveal that our model is suitable for multi-task learning of lesion segmentation, is efficient in low data regimes, and is robust to adversarial attacks.

2022-12-03

Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (publié)

proceedings.mlr.press

Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors

Anjun Hu

Jean-Pierre R. Falet

Changjian Shui

Douglas Arnold

Sotirios A. Tsaftaris

We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain l… (voir plus)esions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternative training configuration where we provide supervision to a subset of latent units. It is shown that (1) a partially supervised latent space achieves a higher degree of disentanglement between evidence of disease and subject-specific anatomy; (2) when the prior is formulated with an autoregressive structure, knowledge from the supervision can propagate to the unsupervised latent units, resulting in more informative latent representations capable of modelling anatomy-pathology interdependencies.

2022-11-14

ArXiv (prépublication)

Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation

Jillian Cardinell

Justin Szeto

Raghav Mehta

Jean-Pierre R. Falet

Douglas L. Arnold

Sotirios A. Tsaftaris

Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, wh… (voir plus)ere unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the "ground-truth" label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified.

2022-10-30

ArXiv (prépublication)

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Raghav Mehta

Angelos Filos

Ujjwal Baid

Chiharu Sako

Richard McKinley

Michael Rebsamen

Katrin Datwyler

Raphael Meier

Piotr Radojewski

Gowtham Krishnan Murugesan

Sahil Nalawade

Chandan Ganesh

Ben Wagner

Fang F. Yu

Baowei Fei

Ananth J. Madhuranthakam

Joseph A. Maldjian

Laura Daza

Catalina Gomez

Pablo Arbelaez … (voir 72 de plus)

Chengliang Dai

Shuo Wang

Hadrien Reynaud

Yuan-han Mo

Elsa Angelini

Yike Guo

Wenjia Bai

Subhashis Banerjee

Lin-min Pei

Murat AK

Sarahi Rosas-Gonzalez

Ilyess Zemmoura

Clovis Tauber

Minh H. Vu

Tufve Nyholm

Tommy Lofstedt

Laura Mora Ballestar

Veronica Vilaplana

Hugh McHugh

Gonzalo Maso Talou

Alan Wang

Jay Patel

Ken Chang

Katharina Hoebel

Mishka Gidwani

Nishanth Arun

Sharut Gupta

Mehak Aggarwal

Praveer Singh

Elizabeth R. Gerstner

Jayashree Kalpathy-Cramer

Nicolas Boutry

Alexis Huard

Lasitha Vidyaratne

Md Monibor Rahman

Khan M. Iftekharuddin

Joseph Chazalon

Elodie Puybareau

Guillaume Tochon

Jun Ma

Mariano Cabezas

Xavier Llado

Arnau Oliver

Liliana Valencia

Sergi Valverde

Mehdi Amian

Mohammadreza Soltaninejad

Andriy Myronenko

Ali Hatamizadeh

Xue Feng

Quan Dou

Nicholas Tustison

Craig Meyer

Nisarg A. Shah

Sanjay Talbar

Marc-André Weber

Abhishek Mahajan

Andras Jakab

Roland Wiest

Hassan M. Fathallah-Shaykh

Arash Nazeri

Mikhail Milchenko1

Daniel Marcus

Aikaterini Kotrotsou

Rivka Colen

John Freymann

Justin Kirby

Christos Davatzikos

Bjoern Menze

Spyridon Bakas

Yarin Gal

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain… (voir plus) Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

2022-08-25

Machine Learning for Biomedical Imaging (publié)

Heatmap Regression for Lesion Detection using Pointwise Annotations

Chelsea Myers-colet

Julien Schroeter

Douglas Arnold

In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as… (voir plus) a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.

2022-08-10

ArXiv (prépublication)

Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers

Amar Kumar

Anjun Hu

Jean-Pierre R. Falet

Douglas Arnold

Sotirios A. Tsaftaris

2022-08-02

ArXiv (prépublication)

Information Gain Sampling for Active Learning in Medical Image Classification

Raghav Mehta

Changjian Shui

2022-07-31

ArXiv (prépublication)

GP.2 Deep learning prediction of response to disease modifying therapy in primary progressive multiple sclerosis

JR Falet

Joshua D. Durso-Finley

Julien Schroeter

Francesca Bovis

M Sormani

D Precup

DL Arnold

Background: Only one disease modifying therapy (DMT), ocrelizumab, was found to slow disability progression in primary progressive multiple … (voir plus)sclerosis (PPMS). Modeling the conditional average treatment effect (CATE) using deep learning could identify individuals more responsive to DMTs, allowing for predictive enrichment to increase the power of future clinical trials. Methods: Baseline clinical and MRI data were acquired as part of three placebo-controlled randomized clinical trials: ORATORIO (ocrelizumab), OLYMPUS (rituximab) and ARPEGGIO (laquinimod). Data from ORATORIO and OLYMPUS was separated into a training (70%) and testing (30%) set, while ARPEGGIO served as additional validation. An ensemble of multitask multilayer perceptrons was trained to predict the rate of disability progression on both treatment and placebo to estimate CATE. Results: The model could separate individuals based on their predicted treatment effect. The top 25% of individuals predicted to respond most have a larger effect size (HR 0.442, p=0.0497) than the entire group (HR 0.787, p=0.292). The model could also identify responders to laquinimod. A simulated study where the 50% most responsive individuals are randomized would require 6-times less participants to detect a significant effect. Conclusions: Individuals with PPMS who respond favourably to DMTs can be identified using deep learning based on their baseline clinical and imaging characteristics.

2022-06-23

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques (publié)

Metrics Reloaded - A new recommendation framework for biomedical image analysis validation

Annika Reinke

Lena Maier-Hein

Evangelia Christodoulou

Ben Glocker

Patrick Scholz

Fabian Isensee

Jens Kleesiek

Michal Kozubek

Mauricio Reyes

Michael Alexander Riegler

Manuel Wiesenfarth

Michael Baumgartner

Matthias Eisenmann

DOREEN HECKMANN-NÖTZEL

Ali Emre Kavur

TIM RÄDSCH

Minu D. Tizabi

Laura Acion

Michela Antonelli

Tal Arbel … (voir 48 de plus)

Spyridon Bakas

Peter Bankhead

Arriel Benis

M. Jorge Cardoso

Veronika Cheplygina

Beth A Cimini

Gary S. Collins

Keyvan Farahani

Bram van Ginneken

Fred A Hamprecht

Daniel A. Hashimoto

Michael M. Hoffman

Merel Huisman

Pierre Jannin

Charles Kahn

Alexandros Karargyris

Alan Karthikesalingam

Hannes Kenngott

Annette Kopp-Schneider

Anna Kreshuk

Tahsin Kurc

Bennett Landman

GEERT LITJENS

Amin Madani

Klaus Maier-Hein

Anne Martel

Peter Mattson

Erik Meijering

Bjoern Menze

David Moher

Karel G.M. Moons

Henning Müller

Felix Nickel

Jens Petersen

Nasir Rajpoot

Nicola Rieke

Julio Saez-Rodriguez

Clara I. Sánchez

Shravya Shetty

Maarten van Smeden

Carole H. Sudre

Ronald M. Summers

Abdel A. Taha

Sotirios A. Tsaftaris

Ben Van Calster

Gael Varoquaux

Paul F Jaeger

Meaningful performance assessment of biomedical image analysis algorithms depends on objective and appropriate performance metrics. There ar… (voir plus)e major shortcomings in the current state of the art. Yet, so far limited attention has been paid to practical pitfalls associated when using particular metrics for image analysis tasks. Therefore, a number of international initiatives have collaborated to offer researchers with guidance and tools for selecting performance metrics in a problem-aware manner. In our proposed framework, the characteristics of the given biomedical problem are first captured in a problem fingerprint, which identifies properties related to domain interests, the target structure(s), the input datasets, and algorithm output. A problem category-specific mapping is applied in the second step to match fingerprints to metrics that reflect domain requirements. Based on input from experts from more than 60 institutions worldwide, we believe our metric recommendation framework to be useful to the MIDL community and to enhance the quality of biomedical image analysis algorithm validation.

2022-05-08

MIDL.io/2022/Conference/Short (accepté)

openreview.net

Deep Learning Prediction of Response to Disease Modifying Therapy in Primary Progressive Multiple Sclerosis (P1-1.Virtual)

Jean-Pierre René Falet

Joshua Durso-finley

Julien Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Arnold

2022-05-02

Neurology (inconnu)

On Learning Fairness and Accuracy on Multiple Subgroups

Changjian Shui

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Boyu Wang

Christian Gagné

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of g… (voir plus)roup sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)

openreview.net

Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning

Jean-Pierre R. Falet

Joshua Durso-finley

Julien Schroeter

Francesca Bovis

Maria-Pia Sormani

Doina Precup

Douglas Lorne Arnold

Disability progression in multiple sclerosis remains resistant to treatment. The absence of a suitable biomarker to allow for phase 2 clinic… (voir plus)al trials presents a high barrier for drug development. We propose to enable short proof-of-concept trials by increasing statistical power using a deep-learning predictive enrichment strategy. Specifically, a multi-headed multilayer perceptron is used to estimate the conditional average treatment effect (CATE) using baseline clinical and imaging features, and patients predicted to be most responsive are preferentially randomized into a trial. Leveraging data from six randomized clinical trials ( n = 3,830), we first pre-trained the model on the subset of relapsing-remitting MS patients ( n = 2,520), then fine-tuned it on a subset of primary progressive MS (PPMS) patients ( n = 695). In a separate held-out test set of PPMS patients randomized to anti-CD20 antibodies or placebo ( n = 297), the average treatment effect was larger for the 50% (HR, 0.492; 95% CI, 0.266-0.912; p = 0.0218) and 30% (HR, 0.361; 95% CI, 0.165-0.79; p = 0.008) predicted to be most responsive, compared to 0.743 (95% CI, 0.482-1.15; p = 0.179) for the entire group. The same model could also identify responders to laquinimod in another held-out test set of PPMS patients ( n = 318). Finally, we show that using this model for predictive enrichment results in important increases in power.

2021-10-31

medRxiv (prépublication)