Audrey Durand

PhD - Université Laval

Bruno Joyal

Master's Research - Université Laval

Alexandre Larouche

PhD - Université Laval

Github

Google Scholar

Randy Lefebvre

Master's Research - Université Laval

Kaoutar Mimouni

Master's Research - Université Laval

Hadi Moazen

PhD - Université Laval

Publications

Development of AI-assisted microscopy frameworks through realistic simulation in pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both the image acquisition… (see more) and analysis phases. Development of artificial intelligence (AI)-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.

2024-09-26

Nature Machine Intelligence (published)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

2024-07-29

bioRxiv (preprint)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

2024-07-29

bioRxiv (preprint)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

The integration of artificial intelligence (AI) into microscopy systems significantly enhances performance, optimizing both the image acquis… (see more)ition and analysis phases. Development of AI-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.

2024-07-29

bioRxiv (preprint)

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Flavie Lavoie-Cardinal

2024-07-29

bioRxiv (preprint)

Randomized Confidence Bounds for Stochastic Partial Monitoring

Ola Ahmad

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

Data harmonization for Advancing research on Personalized Rehabilitation Interventions for Patients with Traumatic Brain Injury and Stroke: A proof of concept

Dorra Rakia Allegue

Despoina Petsani

Nathalie Ponthon

Evdokimos Konstantinidis

Panagiotis Bamidis

Eva Kehayia

Sara Ahmed

Stroke and traumatic brain injury (TBI) are leading causes of morbidity and mortality, affecting survivors’ mobility and social participat… (see more)ion. Although personalized interventions could positively impact survivors' recovery, the effectiveness of such interventions remains unclear. Open-access data repositories can provide access to multiple shared data which could help uncover new evidence of effective interventions; however, harmonizing data between different studies requires many steps to make it possible given the various methods of data collection, intervention characteristics and population sociodemographic profile. This proof-of-concept study aimed to describe the steps and anchors that contributed to the development of guiding frameworks to harmonize data across different studies. Data were extracted from the Federal Interagency Traumatic Brain Injury Research (FITBIR) repository and stored on an online cloud platform. The outcome measures were mapped to mobility determinants using the International Classification of Functioning, Disability, and Health (ICF) and Webber framework. The intervention's effect was categorized according to the Minimal Clinically Important Difference (MCID)s of the measures administered. The study proposed a novel framework for intervention features, which aims to enhance our understanding of the mechanisms of action and potential impact of rehabilitation interventions. The framework classified interventions based on their nature, context, specific body systems, dosage, caregiver assistance, and behaviour change strategies. In conclusion, this study demonstrated the feasibility of harmonizing data extracted from different sources in the FITBIR repository. Leveraging existing open databases offers tremendous opportunities to advance research on personalized interventions for patients with TBI and stroke and inform decision-making during transitions.

2024-06-26

Petra (published)

On shallow planning under partial observability

Randy Lefebvre

2024-05-31

rl-conference.cc/RLC/2024/Workshop/Deployable_RL (published)

Neural Active Learning Meets the Partial Monitoring Framework

Ola Ahmad

We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the … (see more)costly acquisition of information (labelled observations) and the cost of prediction errors. We propose a novel foundation for OAL tasks based on partial monitoring, a theoretical framework specialized in online learning from partially informative actions. We show that previously studied binary and multi-class OAL tasks are instances of partial monitoring. We expand the real-world potential of OAL by introducing a new class of cost-sensitive OAL tasks. We propose NeuralCBP, the first PM strategy that accounts for predictive uncertainty with deep neural networks. Our extensive empirical evaluation on open source datasets shows that NeuralCBP has favorable performance against state-of-the-art baselines on multiple binary, multi-class and cost-sensitive OAL tasks.

2024-04-26

auai.org/UAI/2024/Conference (poster)

Neural Active Learning Meets the Partial Monitoring Framework

Ola Ahmad

2024-04-26

auai.org/UAI/2024/Conference (poster)