Publications

From Speech to Sonography: Spectral Networks for Ultrasound Microstructure Classification
Ali K. Z. Tehrani
An Tang
Mirco Ravanelli
Guy Cloutier
Iman Rafati
Bich Ngoc Nguyen
Quoc-Huy Trinh
Ivan Rosado-Mendez
Hassan Rivaz
The frequency dependence of backscattered radiofrequency (RF) signals produced by ultrasound scanners carries rich information related to th… (voir plus)e tissue microstructure (i.e., scatterer size, attenuation). This information can be sue to classify tissues based on microstructural changes associated to disease onset and progression. Conventional convolutional neural networks (CNNs) can learn this information directly from radio-frequency (RF) data, but they often struggle to achieve adequate frequency selectivity. This increases model complexity and convergence time, and limits generalization. To overcome these challenges, SincNet, originally developed for speech processing, was adapted to classify RF data based on differences in frequency properties. Rather than learning every filter coefficient, SincNet only learns each filter's low frequency and bandwidth, dramatically reducing the number of parameters and improving frequency resolution. For model interpretability, a Gradient-Weighted Filter Contribution is introduced, which highlights the importance of spectral bands. The approach was validated on three datasets: simulated data with different scatterer sizes, experimental phantom data, and in vivo data of rats which were fed a methionine and choline- deficient diet to develop liver steatosis, inflammation, and fibrosis. The modified SincNet consistently achieved the best results in material/tissue classifications.
2025-11-26
IEEE transactions on bio-medical engineering (publié)
doi.org
Synthetic Validation of Pediatric Trust Instruments using Persona-Driven Large Language Models
Ella Boone
Katya Loban
Elena Guadagno
Dan Poenaru
Trust is foundational to patient-physician relationships and is associated with improved care-seeking and adherence in primary care. However… (voir plus), validated trust instruments for pediatric emergency and surgical contexts are lacking, and traditional instrument development is slow and resource-intensive. Large language models (LLMs) could streamline the validation process by serving as scalable, systematic expert panel surrogates. We developed four new trust assessment instruments: two for patient-families and two for physicians. Two-phase content validation was conducted using two parallel synthetic and human expert panels. Synthetic panels consisted of three persona-prompted LLMs (Claude Sonnet 4, GPT-5, Grok4). Human panels served as traditional comparators. Scale-Content Validity Index (S-CVI) and Fleiss’ kappa (k) acceptance thresholds were set at ≥0.80. Combined human–synthetic expert panels revealed substantial inter-rater reliability across all instruments. Fleiss’ kvalues for dimensional validation were: patient-family = 0.84 (95% CI [0.72, 0.96]), physician = 0.87 (95% CI [0.72, 1.00]);contextual validation: patient-family = 0.83 (95% CI [0.73, 0.93]), physician = 0.88 (95% CI [0.80, 0.96]). All instruments exceeded S-CVI ≥0.80 thresholds across both validation phases. Persona-prompted LLMs demonstrated comparable validity outcomes to human experts while accelerating validation timelines from months to weeks. Future research needs to evaluate this approach across psychometric testing phases. This synthetic instrument validation methodology offers a scalable blueprint for healthcare measurement development, enabling faster creation of validated tools to support evidence-based patient care.
2025-11-26
MedRxiv (prépublication)
doi.org
Development of a defacing algorithm to protect the privacy of head and neck cancer patients in publicly-accessible radiotherapy datasets
Kayla O'Sullivan‐Steben
Luc Galarneau
J. Kildea
2025-11-25
Medical Physics (publié)
doi.org
arxiv.org
scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization
Guo Jiang
Kailu Song
Gregory J. Fonseca
Darcy E. Wagner
Iain C. Clark
Hui Wang
Jun Ding
Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust c… (voir plus)ell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary information, such as spatial coordinates, and iteratively refines alignment via self-supervised graph link prediction, where a deep neural network is trained to identify and reinforce high-confidence correspondences across datasets. In extensive benchmarks, scGALA identifies over 25 percent more high-confidence alignments without compromising accuracy. By improving the core step of cell alignment, scGALA serves as a versatile enhancer for a wide range of single-cell data integration tasks.
2025-11-25
Nature Communications (publié)
doi.org
MIMIC-MJX: Neuromechanical Emulation of Animal Behavior
Charles Y. Zhang
Yuanjia Yang
Aidan Sirbu
Elliott T.T. Abe
Emil Wärnberg
Eric J. Leonardis
Diego E. Aldarondo
Adam Lee
Aaditya Prasad
Jason Foat
Kaiwen Bian
Joshua Park
Rusham Bhatt
Hutton Saunders
Akira Nagamori
Ayesha R. Thanawalla
Kee Wui Huang
Fabian Plum
Hendrik K. Beck
Steven W. Flavell … (voir 6 de plus)
David Labonte
Blake A. Richards
Bingni W. Brunton
Eiman Azim
Bence P. Ölveczky
Talmo D. Pereira
The primary output of the nervous system is movement and behavior. While recent advances have democratized pose tracking during complex beha… (voir plus)vior, kinematic trajectories alone provide only indirect access to the underlying control processes. Here we present MIMIC-MJX, a framework for learning biologically-plausible neural control policies from kinematics. MIMIC-MJX models the generative process of motor control by training neural controllers that learn to actuate biomechanically-realistic body models in physics simulation to reproduce real kinematic trajectories. We demonstrate that our implementation is accurate, fast, data-efficient, and generalizable to diverse animal body models. Policies trained with MIMIC-MJX can be utilized to both analyze neural control strategies and simulate behavioral experiments, illustrating its potential as an integrative modeling framework for neuroscience.
2025-11-24
ArXiv (prépublication)
doi.org
arxiv.org
Neural Deprojection of Galaxy Stellar Mass Profiles
M. J. Yantovski-Barth
Hengyue Zhang
Nolan Smyth
Connor Stone
Martin Bureau
Yashar Hezaveh
Laurence Perreault-Levasseur
We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable… (voir plus) mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.
2025-11-24
arXiv (prépublication)
doi.org
arxiv.org
Operationalizing Quantized Disentanglement
Vitória Barin-Pacela
Kartik Ahuja
Simon Lacoste-Julien
P Vincent
2025-11-24
ArXiv (prépublication)
doi.org
arxiv.org
Mind the Information Gap: Unveiling Detailed Morphologies of z 0.5-1.0 Galaxies with SLACS Strong Lenses and Data-Driven Analysis
Ronan Legin
Connor Stone
Alexandre Adam
Gabriel Missael Barco
Adam Coogan
Nikolay Malkin
Laurence Perreault-Levasseur
Yashar Hezaveh
2025-11-23
ArXiv (prépublication)
arxiv.org
arxiv.org
MiRformer: a dual-transformer-encoder framework for predicting microRNA-mRNA interactions from paired sequences
Jiayao Gu
Can (Sam) Chen
Yue Li
MicroRNAs (miRNAs) are small non-coding RNAs that regulate genes by binding to target messenger RNAs (mRNAs), causing them to degrade or sup… (voir plus)pressing their translation. Accurate prediction of miRNA–mRNA interactions is crucial for RNA therapeutics. Existing methods rely on handcrafted features, struggle to scale to kilobase-long mRNA sequences, or lack interpretability. We introduce MiRformer , a transformer framework designed to predict not only the binary miRNA–mRNA interaction but also the start and end location of the miRNA binding site in the mRNA sequence. MiRformer employs a dual-transformer encoder architecture to learn interaction patterns directly from raw miRNA-mRNA sequence pairs via the cross-attention between the miRNA-encoder and mRNA-encoder. To scale to long mRNA sequences, we leverage sliding-window attention mechanism. MiR-former achieves state-of-the-art performance across diverse miRNA–mRNA tasks, including binding prediction, target-site localization, and cleavage-site identification from Degradome sequencing data. The learned transformer attention are highly interpretable and reveals highly contrasting signals for the miRNA seed regions in 500-nt long mRNA sequences. We used MiRformer to simultaneously predict novel binding sites and cleavage sites in 13k miRNA-mRNA pairs and observed that the two types of sites tend to be close to each other, supporting miRNA-mediated degradation mechanism. Our code is available at https://github.com/li-lab-mcgill/miRformer .
2025-11-23
bioRxiv (prépublication)
doi.org
Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images
Connor Stone
Ronan Legin
Alexandre Adam
Nikolay Malkin
Gabriel Missael Barco
Laurence Perreault-Levasseur
Yashar Hezaveh
We introduce a novel framework for upsampled Point Spread Function (PSF) modeling using pixel-level Bayesian inference. Accurate PSF charact… (voir plus)erization is critical for precision measurements in many fields including: weak lensing, astrometry, and photometry. Our method defines the posterior distribution of the pixelized PSF model through the combination of an analytic Gaussian likelihood and a highly expressive generative diffusion model prior, trained on a library of HST ePSF templates. Compared to traditional methods (parametric Moffat, ePSF template-based, and regularized likelihood), we demonstrate that our PSF models achieve orders of magnitude higher likelihood and residuals consistent with noise, all while remaining visually realistic. Further, the method applies even for faint and heavily masked point sources, merely producing a broader posterior. By recovering a realistic, pixel-level posterior distribution, our technique enables the first meaningful propagation of detailed PSF morphological uncertainty in downstream analysis. An implementation of our posterior sampling procedure is available on GitHub.
2025-11-23
ArXiv (prépublication)
arxiv.org
arxiv.org
Publisher Correction: On the compatibility of generative AI and generative linguistics
Eva Portelance
Masoud Jasbi
2025-11-23
Nature Computational Science (publié)
doi.org
Use of an Integrated Knowledge Translation Approach to Develop an Electronic Patient-Reported Outcome System for Cancer Rehabilitation: Tutorial
Christian Lopez
Sarah E Neil-Sztramko
Kristin L Campbell
David M Langelier
Tran Truong
Yuliya Gavrylyuk
Pia Nyakairu
Laura Parente
Audrey Durand
Jackie L Bender
Gillian Strudwick
Rupali Bhati
Jonathan Greenland
Tony Reiman
Jennifer M Jones
Electronic prospective surveillance models (ePSMs) have the potential to improve the management of cancer-related impairments by systematica… (voir plus)lly screening patients using electronic patient-reported outcomes during and after treatment, and linking them to tailored self-management resources and rehabilitation programs. However, their successful implementation into routine care requires careful consideration of patient and provider needs and must align with clinical workflows, which may vary across settings and require adaptation to the local context. The aim of this paper is to describe the development of REACH, a web-based ePSM designed to remotely screen for physical cancer–related impairments and direct patients to rehabilitation resources based on need. The development of REACH followed an integrated knowledge translation (iKT) approach, engaging key knowledge users including patients, clinicians, administrators, and information technology specialists. The development process involved collaboration across 5 working groups. The system content and logic group selected the impairments to be screened, measures used, frequency of screening, and resources recommended based on results of a survey with oncology providers and researchers, patient feedback, a literature review, and an environmental scan. The machine learning group explored predictive modeling approaches to optimize the assessment frequency using retrospective patient data. The implementation group identified features from existing systems that could be built to promote assessment completion and integration into clinical workflows through a scoping review, interviews with clinic staff, and focus groups with patients. The design group conducted co-design workshops and usability testing with patients to iteratively refine the interface and develop a prototype. Finally, the software development group converted the prototype to a web-based application and conducted privacy and security assessments and quality assurance. The integration of key knowledge users through an iKT approach played a critical role in determining the design and functionality of REACH. REACH allows patients to remotely complete assessments tailored to their cancer type and treatment status on any electronic device. The system generates automated advice based on the assessment responses, including links to educational resources for self-management, suggestions for community programs to register for, and recommendations to contact their oncology team for further assessment and possible referral to rehabilitation services. These recommended resources are stored in the patient’s personalized library, organized by type and severity of cancer-related impairments reported, and are updated following each new electronic patient-reported outcomes assessment completed. Additional key system features include a patient-driven and structured process for managing high impairment scores, usability enhancements to improve navigation, and safeguards to ensure data security. The development of REACH demonstrates how an iKT approach can be used to design an ePSM that is user-friendly, clinically relevant, and aligned with implementation considerations. The system has been implemented at 4 Canadian cancer centers, and its implementation is being evaluated to inform future refinements.
2025-11-23
JMIR Cancer (publié)
doi.org