Christian Gagné

Biographie

Christian Gagné est professeur au Département de génie électrique et de génie informatique de l’Université Laval depuis 2008, et dirige l’Institut intelligence et données (IID). Il détient une chaire en IA Canada-CIFAR et est membre associé à Mila – Institut québécois d’intelligence artificielle. Il est également membre du Laboratoire de vision et systèmes numériques (LVSN), une composante du Centre de recherche en robotique, vision et intelligence machine (CeRVIM) ainsi que du Centre de recherche en données massives (CRDM) de l’Université Laval. Il fait partie des regroupements stratégiques REPARTI et UNIQUE du Fonds de recherche du Québec – Nature et technologies (FRQNT), du centre VITAM du Fonds de recherche du Québec – Santé (FRQS) et de l’Observatoire international sur les impacts sociétaux de l’IA et du numérique (OBVIA).

Ses intérêts de recherche portent sur l’élaboration de méthodes pour l’apprentissage automatique et l’optimisation stochastique. En particulier, il se consacre aux réseaux de neurones profonds, à l’apprentissage et au transfert de représentations, au méta-apprentissage ainsi qu’à l’apprentissage multitâche. Il s’intéresse également aux approches d’optimisation basées sur des modèles probabilistes ainsi qu’aux algorithmes évolutionnaires, entre autres pour l’optimisation boîte noire et la programmation automatique. Une part importante de ses travaux porte également sur la mise en pratique de ces techniques dans des domaines comme la vision numérique, la microscopie, la santé, l’énergie et les transports.

Étudiants actuels

Frédéric Beaupré

Doctorat - Université Laval

Catherine Bouchard

Doctorat - Université Laval

Olivier Bussière

Maîtrise recherche - Université Laval

Cynthia García Ybarra

Maîtrise recherche - Université Laval

Sara Karami

Doctorat - Université Laval

Nayoung Kwon

Doctorat - Université Laval

Benjamin Léger

Doctorat - Université Laval

Samy Mammeri

Stagiaire de recherche - Université Laval

Jonas Ngnawe

Doctorat - Université Laval

Fatemeh Nourilenjan Nokabadi

Doctorat - Université Laval

Doctorat - Université Laval

Adam Tupper

Doctorat - Université Laval

Publications

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Daniel Gourdeau

Olivier Potvin

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Reza Forghani

Alexandre Hains

David Hornstein

Huy Le

Simon Lemieux

Marie-Hélène Lévesque

Diego Martin

Lorne Rosenbloom

An Tang

Fabrizio Vecchio

Issac Yang

Nathalie Duchesne

Simon Duchesne

2022-04-04

Scientific Reports (publié)

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Daniel Gourdeau

Olivier Potvin

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Reza Forghani

Alexandre Hains

David Hornstein

Huy Le

Simon Lemieux

Marie-Hélène Lévesque

Diego Martin

Lorne Rosenbloom

An Tang

Fabrizio Vecchio

Issac Yang

Nathalie Duchesne

Simon Duchesne

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity… (voir plus) over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: ‘Worse’, ‘Stable’, or ‘Improved’ on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between “Worse” and “Improved” outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic (‘Consolidation’, ‘Lung Lesion’, ‘Pleural effusion’ and ‘Pneumonia’; all P 0.05). Features from the first CXR of each pair could correctly predict the outcome category between ‘Worse’ and ‘Improved’ cases with a 0.81 (0.74–0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67–0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

2022-04-04

Scientific Reports (publié)

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Daniel Gourdeau

Olivier Potvin

Patrick Archambault

Carl Chartrand‐lefebvre

Louis Dieumegarde

Reza Forghani

Alexandre Hains

David Hornstein

Huy Khiem Le

Simon Lemieux

Marie‐hélène Lévesque

Diego R. Martin

Lorne Rosenbloom

An Tang

Fabrizio Vecchio

Issac Y Yang

N. Duchesne

Simon Duchesne

2022-04-04

Scientific Reports (published)

TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING

Simon Duchesne

Olivier Potvin

Daniel Gourdeau

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Reza Forghani

Alexandre Hains

David Hornstein

Huy Le

Simon Lemieux

Marie-Hélène Lévesque

Diego Martin

Lorne Rosenbloom

An Tang

Fabrizio Vecchio

Issac Yang

Nathalie Duchesne

2022-04-04

Scientific Reports (publié)

Matching Feature Sets for Few-Shot Image Classification

Arman Afrasiyabi

Hugo Larochelle

Jean‐François Lalonde

In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classific… (voir plus)ation methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets-namely miniImageNet, tieredImageNet, and CUB-in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art.

2022-04-02

ArXiv (preprint)

arxiv.org

Active Learning for Capturing Human Decision Policies in a Data Frugal Context

Loïc Grossetête

Alexandre Marois

Bénédicte Chatelais

Daniel Lafond

2022-02-02

Machine Learning, Optimization, and Data Science (publié)

On Learning Fairness and Accuracy on Multiple Subgroups

Changjian Shui

Gezheng Xu

Qi CHEN

Jiaqi Li

Charles Ling

Tal Arbel

Boyu Wang

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.

openreview.net

On the benefits of representation regularization in invariance based domain generalization

Changjian Shui

Boyu Wang

2022-01-01

Machine Learning (publié)