Publications

PedMedQA: Comparing Large Language Model Accuracy in Pediatric and Adult Medicine
Nikhil Jaiswal
Yuanchao Ma
Bertrand Lebouché
Dan Poenaru
Esli Osmanlliu
Large language models (LLMs) have the potential to revolutionize healthcare, including aiding in clinical decision-making. However, recent w… (voir plus)ork suggests that LLM performance in pediatric cases may be weaker than adult cases. A key limitation in evaluating these differences is the lack of pediatric-specific benchmarks, making it difficult to systematically assess how well LLMs generalize to pediatric scenarios.
2025-05-18
Pediatrics Open Science (publié)
doi.org
RobusTAD: reference panel based annotation of nested topologically associating domains
Yanlin Zhang
Rola Dali
Mathieu Blanchette
Topologically associating domains (TADs) are fundamental units of 3D genomes and play essential roles in gene regulation. Hi-C data suggests… (voir plus) a hierarchical organization of TADs. Accurately annotating nested TADs from Hi-C data remains challenging, both in terms of the precise identification of boundaries and the correct inference of hierarchies. While domain boundary is relatively well conserved across cells, few approaches have taken advantage of this fact. Here, we present RobusTAD to annotate TAD hierarchies. It incorporates additional Hi-C data to refine boundaries annotated from the study sample. RobusTAD outperforms existing tools at boundary and domain annotation across several benchmarking tasks. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-025-03568-9.
2025-05-18
Genome Biology (publié)
doi.org
Seeing the Unseen: How EMoE Unveils Bias in Text-to-Image Diffusion Models
Lucas Berry
Axel Brando
Wei-Di Chang
Juan Higuera
David Meger
2025-05-18
ArXiv (prépublication)
doi.org
openreview.net
Topological mapping for traversability-aware long-range navigation in off-road terrain
Jean-François Tremblay
Julie Alhosh
Louis Petit
Faraz Lotfi
Lara Landauro
David Meger
Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (voir plus)ied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 m2 forest sites unseen during training, in difficult conditions for navigation.
2025-05-18
2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting
He Zhu
Jun Bai
Na Li
Xiaoxiao Li
Dianbo Liu
David L. Buckeridge
Yue Li
Federated learning (FL) enables collaborative analysis of decentralized medical data while preserving patient privacy. However, the covariat… (voir plus)e shift from demographic and clinical differences can reduce model generalizability. We propose FedWeight, a novel FL framework that mitigates covariate shift by reweighting patient data from the source sites using density estimators, allowing the trained model to better align with the distribution of the target site. To support unsupervised applications, we introduce FedWeight ETM, a federated embedded topic model. We evaluated FedWeight in cross-site FL on the eICU dataset and cross-dataset FL between eICU and MIMIC III. FedWeight consistently outperforms standard FL baselines in predicting ICU mortality, ventilator use, sepsis diagnosis, and length of stay. SHAP-based interpretation and ETM-based topic modeling reveal improved identification of clinically relevant characteristics and disease topics associated with ICU readmission.
2025-05-16
NPJ Digital Medicine (publié)
doi.org
Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Minsu Kim
Jean-Pierre R. Falet
Oliver E. Richardson
Xiaoyin Chen
Moksh J. Jain
Sungjin Ahn
Sungsoo Ahn
Yoshua Bengio
Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can cont… (voir plus)ain inaccurate statements that reduce performance and trustworthiness. To address this, we propose to augment each reasoning step in a CoT with a latent veracity (or correctness) variable. To efficiently explore this expanded space, we introduce Veracity Search (VS), a discrete search algorithm over veracity assignments. It performs otherwise intractable inference in the posterior distribution over latent veracity values by leveraging the LM's joint likelihood over veracity and the final answer as a proxy reward. This efficient inference-time verification method facilitates supervised fine-tuning of an Amortized Veracity Inference (AVI) machine by providing pseudo-labels for veracity. AVI generalizes VS, enabling accurate zero-shot veracity inference in novel contexts. Empirical results demonstrate that VS reliably identifies errors in logical (ProntoQA), mathematical (GSM8K), and commonsense (CommonsenseQA) reasoning benchmarks, with AVI achieving comparable zero-shot accuracy. Finally, we demonstrate the utility of latent veracity inference for providing feedback during self-correction and self-improvement.
2025-05-16
ArXiv (prépublication)
doi.org
arxiv.org
A multi-ancestry genetic reference for the Quebec population
Peyton McClelland
Georgette Femerling
Rose Laflamme
Alejandro Mejia-Garcia
Mohadese Sayahian Dehkordi
Hongyu Xiao
Alex Diaz-Papkovich
Justin Pelletier
Jean-Christophe Grenier
Ken Sin Lo
Luke Anderson-Trocmé
Justin Bellavance
Vincent Chapdelaine
Genevieve Gagnon
Annelie De Mori
Gerardo Martinez
Kristen Mohler
Thibault de Malliard
Catherine Labbé
Marjorie Labrecque … (voir 14 de plus)
Alexandre Montpetit
Dan Spiegelman
Guy A Rouleau
Jean-François Théroux
Hufeng Zhou
Simon L Girard
Julie G Hussin
Anne-Marie Laberge
Claude Bhérer
Martine Tétreault
Sarah A Gagliano Taliun
Daniel Taliun
Simon Gravel
Guillaume Lettre
While international efforts have characterized genetic variation in millions of individuals, the interplay of environmental, social, cultura… (voir plus)l, and genetic factors is poorly understood for most worldwide populations. The province of Quebec in Canada has been the site of numerous genetic studies, often focusing on individual Mendelian diseases in founder sub-populations. Here, we profiled and analyzed genome-wide genotyped variation in 29,337 Quebec residents from the large population-based cohort CARTaGENE (CaG), including rich phenotype and environmental data. We also sequenced the whole-genome of 2,173 CaG participants, including 163 and 132 individuals with grandparents born in Haiti and Morocco, respectively. We use this genetic information to gain insight into Quebec's demography and to help interpret the potential significance of variants identified in clinically important genes. We built an imputation panel by phasing the CaG whole-genome sequence data and showed, using genome-wide association studies (GWAS), how it improves the discovery of phenotype-genotype associations in this population. We provide allele frequency information and GWAS results through dedicated and publicly available websites. The genetic data, paired with phenotypic and environmental information, is also available for research use upon scientific and ethical review.
2025-05-15
medRxiv (prépublication)
doi.org
Persistent signs of poisoning after massive drug ingestion: move the ultrasound probe to the stomach.
N. Lautrou-cabasson
H. Pirollet
C. Lombois
Guillaume Dumas
2025-05-14
Intensive Care Medicine (publié)
doi.org
The CASTOR mission
Patrick Côté
T. Woods
John Hutchings
J. Rhodes
R. Sánchez-Janssen
Alan D. Scott
J. Pazder
Melissa Amenouche
Michael Balogh
Simon Blouin
Alain Cournoyer
M. Drout
Nick Kuzmin
Katherine J. Mack
Laura Ferrarese
Wesley C. Fraser
S. Gallagher
Frederic J. Grandmont
Daryl Haggard
P. Harrison … (voir 160 de plus)
V. Hénault-Brunet
J. Kavelaars
V. Khatu
J. Roediger
J. Rowe
Marcin Sawicki
Jesper Skottfelt
Matt Taylor
L. van Waerbeke
Laurie Amen
Dhananjhay Bansal
Martin Bergeron
Toby Brown
Greg Burley
Hum Chand
Isaac Cheng
Ryan Cloutier
N. Dickson
Oleg Djazovski
Ivana Damjanov
James Doherty
K. Finner
Macarena García Del Valle Espinosa
Jennifer Glover
A. I. Gómez de Castro
Or Graur
Tim Hardy
Michelle Kao
D A Leahy
Deborah Lokhorst
A. I. Malz
Allison Man
Madeline A. Marshall
Sean McGee
Ryan McKenzie
Kai Michaud
Surhud S. More
David Morris
Patrick W. Morris
T. Moutard
Wasi Naqvi
Matthew Nicholl
G. Noirot
M. S. Oey
C. Opitom
Samir Salim
Bryan R. Scott
Charles Shapiro
Daniel Stern
Ashwin Subramaniam
David Thilke
I. Wevers
Dmitri Vorobiev
L. Y. Aaron Yung
Frédéric Zamkotsian
S. Aigrain
A. Alavi
Martin Barstow
Peter Bartosik
H. Bluhm
J. Bovy
Peter Cameron
R. Carlberg
J. Christiansen
Yuyang Chen
P. Crowther
Kristen Dage
Aaron Dotter
Patrick Dufour
Jean Dupuis
B. Dryer
A. Duara
Gwendolyn M. Eadie
Marielle R. Eduardo
V. Estrada-Carpenter
Sébastien Fabbro
A. Faisst
N. M. Ford
M. Fraser
Boris T. Gaensicke
Shashkiran Ganesh
Poshak Gandhi
Melissa L. Graham
R. Hamel
Martin Hellmich
John J. Hennessy
Kaitlyn Hessel
J. Heyl
Catherine Heymans
Yashar Hezaveh
Renée Hložek
Michael Hoenk
Andrew Holland
Eric Huff
Ian Hutchinson
I. Iwata
April D. Jewell
Doug Johnstone
Maia Jones
Todd J. Jones
D. Lang
J. Lapington
Justin Larivière
C. Lawlor-Forsyth
Denis Laurin
Charles Lee
Ronan Legin
Ting S. Li
S. Lim
B. Ludwig
Matt Kozun
V. M
Robert Mann
Alan McConnachie
Evan McDonough
S. Metchev
David R. Miller
Takashi Moriya
Cameron Morgan
Julio F. Navarro
Y. Nazé
Shouleh Nikzad
Vivek Oad
N. N.-Q. Ouellette
E. Pass
Will J. Percival
Laurence Perreault-Levasseur
Joe Postma
Nayyer Raza
G. T. Richards
Harvey Richer
Carmelle Robert
Erik Rosolowsky
J. Ruan
Sarah Rugheimer
S. Safi-Harb
Kanak Saha
Vicky Scowcroft
F. Sestito
Himanshu Sharma
James Sikora
G. Sivakoff
T. S. Sivarani
Patrick Smith
Warren Soh
R. Sorba
S. Subramanian
Hossen Teimoorinia
H. Teplitz
Shaylin Thadani
Shavon Thadani
Aaron Tohuvavohu
K. Venn
Nicholas Vieira
Jeremy J. Webb
P. Wiegert
Ryan Wierckx
Yanqin Wu
J. Yeung
S. K. Yi
2025-05-13
Journal of Astronomical Telescopes Instruments and Systems (publié)
doi.org
Influence of scanning plane on Human Spinal Cord functional Magnetic Resonance echo planar imaging
Marta Moraschi
Silvia Tommasin
Laura Maugeri
Mauro DiNuzzo
Julien Cohen-Adad
Marco Masullo
Fabio Mangini
Lorenzo Giovannelli
Daniele Mascali
Tommaso Gili
Valerio Pisani
Ugo Nocentini
Federico Giove
Michela Fratini
BACKGROUND: Functional Magnetic Resonance Imaging (fMRI) is based on the Blood Oxygenation Level Dependent contrast and has been exploited f… (voir plus)or the indirect study of the neuronal activity within both the brain and the spinal cord. However, the interpretation of spinal cord fMRI (scfMRI) is still controversial and its diffusion is rather limited because of technical limitations. Overcoming these limitations would have a beneficial effect for the assessment and follow-up of spinal injuries and neurodegenerative diseases. PURPOSE: This study was aimed at systematically verify whether sagittal scanning in scfMRI using EPI readout is a viable alternative to the more common axial scanning, and at optimizing a pipeline for EPI-based scfMRI data analysis, based on Spinal Cord Toolbox (SCT). METHODS: Forty-five healthy subjects underwent MRI acquisition in a Philips Achieva 3T MRI scanner. T2*-weighted fMRI data were acquired using a GE-EPI sequence along sagittal and axial planes during an isometric motor task. Differences on benchmarks were assessed via paired two-sample t-test at p=0.05. RESULTS: We investigated the impact of the acquisition strategy by means of various metrics such as Temporal Signal to Noise Ratio (tSNR), Dice Coefficient to assess geometric distortions, Reproducibility and Sensitivity. tSNR was higher in axial than in sagittal scans, as well as reproducibility within the whole cord mask (t=7.4, p0.01) and within the GM mask (t=4.2, p0.01). The other benchmarks, associated with distortion and functional response, showed no differenc
2025-05-11
PLOS One (publié)
doi.org
arxiv.org
Learning Penalty for Optimal Partitioning via Automatic Feature Extraction
Tung L. Nguyen
Toby Dylan Hocking
Changepoint detection identifies significant shifts in data sequences, making it important in areas like finance, genetics, and healthcare. … (voir plus)The Optimal Partitioning algorithms efficiently detect these changes, using a penalty parameter to limit the changepoints number. Determining the appropriate value for this penalty can be challenging. Traditionally, this process involved manually extracting statistical features, such as sequence length or variance to make the prediction. This study proposes a novel approach that uses recurrent neural networks to learn this penalty directly from raw sequences by automatically extracting features. Experiments conducted on 20 benchmark genomic datasets show that this novel method surpasses traditional methods in partitioning accuracy in most cases.
2025-05-11
ArXiv (prépublication)
doi.org
arxiv.org
OpenLex3D: A New Evaluation Benchmark for Open-Vocabulary 3D Scene Representations
Christina Kassab
Sacha Morin
Martin Büchner
Matias Mattamala
Kumaraditya Gupta
Abhinav Valada
Liam Paull
Maurice Fallon
2025-05-11
IEEE.org/ICRA/2025/Workshop/Safe-VLM (spotlight)
openreview.net
openreview.net