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

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

Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec

Fatemeh Gholi Zadeh Kharrat

Alain Lesage

Geneviève Gariépy

Jean-François Pelletier

Camille Brousseau-Paradis

Louis Rochette

Eric Pelletier

Pascale Lévesque

Mada Mohammed

Jianli Wang

Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine … (voir plus)learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models’ output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.

2024-04-03

PLOS ONE (publié)

Generalizing across Temporal Domains with Koopman Operators

Qiuhao Zeng

Wei Wang

Fan Zhou

Gezheng Xu

Ruizhi Pu

Changjian Shui

Shichun Yang

Boyu Wang

Charles Ling

2024-03-24

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound Images

Adam Tupper

Data augmentation is one of the most effective techniques to improve the generalization performance of deep neural networks. Yet, despite of… (voir plus)ten facing limited data availability in medical image analysis, it is frequently underutilized. This appears to be due to a gap in our collective understanding of the efficacy of different augmentation techniques across medical imaging tasks and modalities. One domain where this is especially true is breast ultrasound images. This work addresses this issue by analyzing the effectiveness of different augmentation techniques for the classification of breast lesions in ultrasound images. We assess the generalizability of our findings across several datasets, demonstrate that certain augmentations are far more effective than others, and show that their usage leads to significant performance gains.

2024-03-14

ArXiv (prépublication)

Hessian Aware Low-Rank Weight Perturbation for Continual Learning

Jiaqi Li

Rui Wang

Yuanhao Lai

Changjian Shui

Sabyasachi Sahoo

Charles Ling

Shichun Yang

Boyu Wang

Fan Zhou

Continual learning aims to learn a series of tasks sequentially without forgetting the knowledge acquired from the previous ones. In this wo… (voir plus)rk, we propose the Hessian Aware Low-Rank Perturbation algorithm for continual learning. By modeling the parameter transitions along the sequential tasks with the weight matrix transformation, we propose to apply the low-rank approximation on the task-adaptive parameters in each layer of the neural networks. Specifically, we theoretically demonstrate the quantitative relationship between the Hessian and the proposed low-rank approximation. The approximation ranks are then globally determined according to the marginal increment of the empirical loss estimated by the layer-specific gradient and low-rank approximation error. Furthermore, we control the model capacity by pruning less important parameters to diminish the parameter growth. We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method. Empirical results show that our method performs better on different benchmarks, especially in achieving task order robustness and handling the forgetting issue. The source code is at https://github.com/lijiaqi/HALRP.

2024-01-01

IEEE Trans. Knowl. Data Eng. (publié)

Filtering Pixel Latent Variables for Unmixing Noisy and Undersampled Volumetric Images

Andréanne Deschênes

Vincent Boulanger

Jean-Michel Bellavance

Julia Chabbert

Alexy Pelletier-Rioux

Flavie Lavoie-Cardinal

2023-12-08

ArXiv (prépublication)

Unmixing Optical Signals from Undersampled Volumetric Measurements by Filtering the Pixel Latent Variables

Andréanne Deschênes

Vincent Boulanger

Jean-Michel Bellavance

Julia Chabbert

Alexy Pelletier-Rioux

Flavie Lavoie-Cardinal

2023-12-08

ArXiv (prépublication)

Unmixing Optical Signals from Undersampled Volumetric Measurements by Filtering the Pixel Latent Variables

Andréanne Deschênes

Vincent Boulanger

Jean-Michel Bellavance

Julia Chabbert

Alexy Pelletier-Rioux

Flavie Lavoie-Cardinal

The development of signal unmixing algorithms is essential for leveraging multimodal datasets acquired through a wide array of scientific im… (voir plus)aging technologies, including hyperspectral or time-resolved acquisitions. In experimental physics, enhancing the spatio-temporal resolution or expanding the number of detection channels often leads to diminished sampling rate and signal-to-noise ratio (SNR), significantly affecting the efficacy of signal unmixing algorithms. We propose Latent Unmixing, a new approach which applies band-pass filters to the latent space of a multi-dimensional convolutional neural network to disentangle overlapping signal components. It enables better isolation and quantification of individual signal contributions, especially in the context of undersampled distributions. Using multi-dimensional convolution kernels to process all dimensions simultaneously enhances the network's ability to extract information from adjacent pixels, and time- or spectral-bins. This approach enables more effective separation of components in cases where individual pixels do not provide clear, well-resolved information. We showcase the method's practical use in experimental physics through two test cases that highlight the versatility of our approach: fluorescence lifetime microscopy and mode decomposition in optical fibers. The latent unmixing method extracts valuable information from complex signals that cannot be resolved by standard methods. It opens new possibilities in optics and photonics for multichannel separations at an increased sampling rate.

2023-12-08

ArXiv (prépublication)

Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning

Jiaqi Li

Rui Wang

Yuanhao Lai

Changjian Shui

Sabyasachi Sahoo

Charles Ling

Shichun Yang

Boyu Wang

Fan Zhou

2023-11-26

ArXiv (prépublication)

Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning

Jiaqi Li

Rui Wang

Yuanhao Lai

Changjian Shui

Sabyasachi Sahoo

Charles Ling

Shichun Yang

Boyu Wang

Fan Zhou

2023-11-26

ArXiv (prépublication)

Towards More General Loss and Setting in Unsupervised Domain Adaptation

Changjian Shui

Ruizhi Pu

Gezheng Xu

Jun Wen

Fan Zhou

Charles Ling

Boyu Wang

In this article, we present an analysis of unsupervised domain adaptation with a series of theoretical and algorithmic results. We derive a … (voir plus)novel Rényi-

2023-10-01

IEEE Transactions on Knowledge and Data Engineering (publié)

Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition

Theresa Wiesner

Andréanne Deschênes

Anthony Bilodeau

Benoit Turcotte

Flavie Lavoie-Cardinal

2023-07-27

Nature Machine Intelligence (publié)

Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning

Mohamed Abderrahmen Abid

Arman Afrasiyabi

Ihsen Hedhli

Jean‐François Lalonde

2023-05-08

ArXiv (prépublication)