Publications

Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent
Luca Della Libera
Jacopo Andreoli
Davide Dalle Pezze
Mirco Ravanelli
Gian Antonio Susto
A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has … (see more)improved considerably upon traditional model-based and statistical approaches in terms of predictive performance. However, in order to optimally plan maintenance operations, it is also important to quantify the uncertainty inherent to the predictions. This issue can be addressed by turning standard frequentist neural networks into Bayesian neural networks, which are naturally capable of providing confidence intervals around the estimates. Several methods exist for training those models. Researchers have focused mostly on parametric variational inference and sampling-based techniques, which notoriously suffer from limited approximation power and large computational burden, respectively. In this work, we use Stein variational gradient descent, a recently proposed algorithm for approximating intractable distributions that overcomes the drawbacks of the aforementioned techniques. In particular, we show through experimental studies on simulated run-to-failure turbofan engine degradation data that Bayesian deep learning models trained via Stein variational gradient descent consistently outperform with respect to convergence speed and predictive performance both the same models trained via parametric variational inference and their frequentist counterparts trained via backpropagation. Furthermore, we propose a method to enhance performance based on the uncertainty information provided by the Bayesian models. We release the source code at https://github.com/lucadellalib/bdl-rul-svgd.
2024-02-02
ArXiv (preprint)
doi.org
arxiv.org
Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model for Influence Operations on Online Social Media
Jwen Fai Low
Benjamin Fung
Farkhund Iqbal
Claude Fachkha
2024-02-02
ArXiv (preprint)
doi.org
arxiv.org
A database of the healthy human spinal cord morphometry in the PAM50 template space
Jan Valošek
Sandrine Bédard
Miloš Keřkovský
Tomáš Rohan
Julien Cohen-Adad
Abstract Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of … (see more)spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher.
2024-02-02
Imaging Neuroscience (published)
doi.org
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
Mohammad Javad Darvishi Bayazi
Mohammad S. Ghaemi
Timothee LESORT
Md Rifat Arefin
Jocelyn Faubert
Irina Rish
2024-02-01
Computers in Biology and Medicine (published)
doi.org
arxiv.org
Data science opportunities of large language models for neuroscience and biomedicine
Danilo Bzdok
Andrew Thieme
Oleksiy Levkovskyy
Paul Wren
Thomas Ray
Siva Reddy
2024-02-01
Neuron (published)
doi.org
Family‐centred care interventions for children with chronic conditions: A scoping review
Andrea J. Chow
Ammar Saad
Zobaida Al‐Baldawi
Ryan Iverson
Becky Skidmore
Isabel Jordan
Nicole Pallone
Maureen Smith
Pranesh Chakraborty
Jamie Brehaut
Eyal Cohen
Sarah Dyack
Jane Gillis
Sharan Goobie
Cheryl Greenberg
Robin Hayeems
Brian Hutton
Michal Inbar-Feigenberg
Shailly Jain-Ghai
Sara Khangura … (see 18 more)
Jennifer MacKenzie
John J. Mitchell
Zeinab Moazin
Stuart G. Nicholls
Amy Pender
Chitra Prasad
Andreas Schulze
Komudi Siriwardena
Rebecca N. Sparkes
Kathy N. Speechley
Sylvia Stockler
Monica Taljaard
Mari Teitelbaum
Yannis Trakadis
Clara Van Karnebeek
Jagdeep S. Walia
Kumanan Wilson
Beth K. Potter
2024-02-01
Health Expectations (published)
doi.org
Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source
Ariane Marelli
Chao Li
Aihua Liu
Hanh Nguyen
Harry Moroz
James M. Brophy
Liming Guo
David Buckeridge
Jian Tang
Archer Yang
Yue Li
2024-02-01
JACC: Advances (published)
doi.org
Neural semantic tagging for natural language-based search in building information models: Implications for practice
Mehrzad Shahinmoghadam
Samira Ebrahimi Kahou
Ali Motamedi
2024-02-01
Computers in industry (Print) (published)
doi.org
Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis.
Waseem Abu-Ashour
Sherif Emil
Sister Mary Emil
Dan Poenaru
2024-02-01
Journal of Pediatric Surgery (published)
doi.org
A novel and efficient machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition
Marc-andr'e Legault
Jason Hartford
Benoît J. Arsenault
Y. Archer
Yang
Joelle Pineau
Mendelian Randomization (MR) enables estimation of causal effects while controlling for unmeasured confounding factors. However, traditional… (see more) MR's reliance on strong parametric assumptions can introduce bias if these are violated. We introduce a new machine learning MR estimator named Quantile Instrumental Variable (IV) that achieves low estimation error in a wide range of plausible MR scenarios. Quantile IV is distinctive in its ability to estimate nonlinear and heterogeneous causal effects and offers a flexible approach for subgroup analysis. Applying Quantile IV, we investigate the impact of circulating sclerostin levels on heel bone mineral density, osteoporosis, and cardiovascular outcomes in the UK Biobank. Employing various MR estimators and colocalization techniques that allow multiple causal variants, our analysis reveals that a genetically predicted reduction in sclerostin levels significantly increases heel bone mineral density and reduces the risk of osteoporosis, while showing no discernible effect on ischemic cardiovascular diseases. Quantile IV contributes to the advancement of MR methodology, and the case study on the impact of circulating sclerostin modulation contributes to our understanding of the on-target effects of sclerostin inhibition.
2024-01-31
medRxiv (preprint)
doi.org
Blockwise Self-Supervised Learning at Scale
Shoaib Ahmed Siddiqui
David Scott Krueger
Yann LeCun
Stephane Deny
2024-01-30
TMLR (accepted)
doi.org
openreview.net
Integrating accompanying patients into clinical oncology teams: limiting and facilitating factors
Marie-Pascale Pomey
Jesseca Paquette
Monica Iliescu Nelea
Cécile Vialaron
Rim Mourad
Karine Bouchard
Louise Normandin
Marie‐Andrée Côté
Mado Desforges
Pénélope Pomey‐Carpentier
Israël Fortin
Isabelle Ganache
Catherine Régis
Zeev Rosberger
Danielle Charpentier
Marie-France Vachon
Lynda Bélanger
Michel Dorval
Djahanchah Philip Ghadiri
Mélanie Lavoie-Tremblay … (see 5 more)
Antoine Boivin
Jean-François Pelletier
Nicolas Fernandez
Alain M. Danino
Michèle de Guise
2024-01-30
BMC Health Services Research (published)
doi.org