Christian Gagné

Biography

Christian Gagné has been a professor in the Department of Electrical and Computer Engineering at Université Laval since 2008.

He is the director of the Institute Intelligence and Data (IID), holds a Canada CIFAR AI Chair, and is an associate member of Mila – Quebec Artificial Intelligence Institute.

Gagné is also a member of Université Laval’s Computer Vision and Systems Laboratory (LVSN), as well as its Robotics, Vision and Machine Intelligence Research Centre (CeRVIM) and its Big Data Research Centre (CRDM). He is a member of the REPARTI and UNIQUE strategic clusters of the FRQNT, the VITAM centre of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technologies (OBVIA).

Gagné’s research focuses on the development of methods for machine learning and stochastic optimization. In particular, he is interested in deep neural networks, representation learning and transfer, meta-learning and multitasking. He is also interested in optimization approaches based on probabilistic models and evolutionary algorithms, including black-box optimization and automatic programming. An important part of his work is the practical application of these techniques in fields like computer vision, microscopy, healthcare, energy and transportation.

Current Students

Frédéric Beaupré

PhD - Université Laval

Catherine Bouchard

PhD - Université Laval

Olivier Bussière

Master's Research - Université Laval

olivier2202@gmail.com

Cynthia García Ybarra

Master's Research - Université Laval

Sara Karami

PhD - Université Laval

Benjamin Léger

PhD - Université Laval

Samy Mammeri

Research Intern - Université Laval

Jonas Ngnawe

PhD - Université Laval

Fatemeh Nourilenjan Nokabadi

PhD - Université Laval

PhD - Université Laval

Adam Tupper

PhD - Université Laval

adam.tupper.1@ulaval.ca

Publications

Lifelong Online Learning from Accumulated Knowledge

Changjian Shui

William Wang

Ihsen Hedhli

Chi Man Wong

Feng Wan

Boyu Wang

In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task… (see more) depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online learning, where the learning process for each task is in an incremental manner. Specifically, our framework is composed of two-level predictions: the prediction information that is solely from the current task; and the prediction from the knowledge base by previous tasks. Moreover, this article tackled several fundamental challenges: arbitrary or even non-stationary task generation process, an unknown number of instances in each task, and constructing an efficient accumulated knowledge base. Notably, we provide a provable bound of the proposed algorithm, which offers insights on the how the accumulated knowledge improves the predictions. Finally, empirical evaluations on both synthetic and real datasets validate the effectiveness of the proposed algorithm.

2022-10-17

ACM Transactions on Knowledge Discovery from Data (published)

The 5-year longitudinal diagnostic profile and health services utilization of patients treated with electroconvulsive therapy in Quebec: a population-based study

Simon Lafrenière

Fatemeh Gholi-Zadeh-Kharrat

Caroline Sirois

Victoria Massamba

Louis Rochette

Camille Brousseau-Paradis

Simon Patry

Morgane Lemasson

Geneviève Gariépy

Chantal Mérette

Elham Rahme

Alain Lesage

2022-09-26

Social Psychiatry and Psychiatric Epidemiology (published)

A novel domain adaptation theory with Jensen-Shannon divergence

Changjian Shui

Qi CHEN

Jun Wen

Fan Zhou

Boyu Wang

2022-09-01

Knowledge-Based Systems (published)

Fair Representation Learning through Implicit Path Alignment

Changjian Shui

Qi CHEN

Jiaqi Li

Boyu Wang

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (published)

openreview.net

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… (see more)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.

2022-06-18

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

arxiv.org

Evolving Domain Generalization

Wei Wang

Gezheng Xu

Ruizhi Pu

Jiaqi Li

Fan Zhou

Changjian Shui

Charles Ling

Boyu Wang

2022-05-31

ArXiv (preprint)

arxiv.org

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients

Daniel Gourdeau

Olivier Potvin

Jason Henry Biem

Florence Cloutier

Lyna Abrougui

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Louis Gagnon

Raphaelle Giguère

Alexandre Hains

Huy Le

Simon Lemieux

Marie-Hélène Lévesque

Simon Nepveu

Lorne Rosenbloom

An Tang

Issac Yang

Nathalie Duchesne … (see 1 more)

Simon Duchesne

2022-04-13

Scientific Reports (published)

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 (published)

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 (published)

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… (see more) 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 (published)

Matching Feature Sets for Few-Shot Image Classification

Arman Afrasiyabi

Hugo Larochelle

Jean‐François Lalonde

2022-04-02

ArXiv (preprint)