Portrait de Hassan Rivaz

Hassan Rivaz

Membre académique associé
hassan.rivaz@concordia.ca
Concordia University, Génie électrique et informatique
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage multimodal
Apprentissage profond
IA appliquée
IA en santé
IA et santé
Imagerie médicale
Imagerie sonar
Modèles génératifs
Modèles probabilistes
Vision par ordinateur
XAI (IA explicable)
Site web
Google Scholar
LinkedIn

Publications

Recursive Vision Language Models for General Symbolic Reasoning
Omid Nejati Manzari
Guillaume Lajoie
Hassan Rivaz
Hard symbolic-reasoning tasks such as Sudoku, maze pathfinding, and ARC remain challenging for LLMs due to their fixed-depth autoregressive … (voir plus)reasoning, which limits systematic search, refinement, and backtracking. While recursive models such as Hierarchical Reasoning Model (HRM) and Tiny Recursive Model (TRM) address this limitation through iterative latent-state refinement, they are typically task-specific and do not leverage pretrained language priors. We propose R-Qwen, a recursive reasoning framework built upon a pretrained Qwen backbone. R-Qwen repeatedly refines a candidate solution through programmatic self-recursion and deep supervision, combining the structured iterative computation of recursive models with the linguistic and reasoning priors of pretrained LLMs. We further adapt Hierarchical Supervision Weighting (HSW) to autoregressive models by exponentially weighting losses across recursive steps. HSW reduces gradient variance by at least 50\%, improves the signal-to-noise ratio of stochastic gradients, and accelerates convergence. Across eight challenging benchmarks, R-Qwen consistently outperforms prior recursive reasoning models and substantially larger LLMs while using a comparable number of trainable parameters. Notably, on ARC-AGI dataset, our model achieves a 27.6\% improvement over the baseline, highlighting the effectiveness of recursive refinement for general symbolic reasoning. These results suggest that recursive reasoning mechanisms and pretrained language model priors are complementary approaches for improving symbolic puzzle-solving. Code and models will be released after acceptance.
2026-08-01
arXiv (prépublication)
doi.org
arxiv.org
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
Ultrasound and MRI-based evaluation of relationships between morphological and mechanical properties of the lower lumbar multifidus muscle in chronic low back pain
Neda Naghdi
Sara Masi
Cleo Bertrand
Brent Rosenstein
Julien Cohen-Adad
Hassan Rivaz
Mathieu Roy
Maryse Fortin
While lumbar multifidus (MF) muscle alterations are linked to low back pain (LBP), the structure-function relationship is not fully understo… (voir plus)od. This study aims to evaluate the relationship between fatty degeneration of the lumbar MF muscle and its function in individuals with and without LBP. The study included 25 participants with chronic nonspecific LBP and 25 age- and sex-matched healthy controls. Participants underwent MRI assessment for MF fat infiltration, utilizing IDEAL fat-water images. Ultrasound measures evaluated MF function, including shear-wave elastography (SWE) for stiffness/elasticity and thickness ratio from rest to submaximal contraction. All measurements were acquired at L4/L5 and L5/S1 spinal levels, bilaterally. Bivariate and multivariable linear regression models were used to assess the relationship between morphology and function, while age, sex, body max index (BMI), physical activity levels, and LBP status were considered as covariates. Fifty participants (26 females) were included (mean age: 39.22 ± 11.67). Greater % MF fat at L4/L5 was significantly associated with greater MF SWE ratio (p = 0.002). No significant bivariate or multivariable relationships were found between MF fat infiltration and MF thickness ratio. Participants with LBP exhibited lower contraction ratios (p = 0.017) and higher SWE during contraction (p = 0.03) at L4/L5 compared to controls. This study highlights a positive association between MF fat infiltration and SWE-based stiffness measures at L4/L5, suggesting altered muscle composition may impacts MF function. However, no relationship was found between MF fat infiltration and contraction. Participants with LBP demonstrated distinct deficits in muscle activation, supporting the need for targeted rehabilitation strategies addressing these functional impairments.
2025-06-08
European Spine Journal (publié)
doi.org
Comparative Analysis of Diffusion Generative Models in Computational Pathology
Denisha Thakkar
Vincent Quoc-Huy Trinh
Sonal Varma
S Ebrahimi Kahou
Hassan Rivaz
Mahdi S. Hosseini
Diffusion Generative Models (DGM) have rapidly surfaced as emerging topics in the field of computer vision, garnering significant interest a… (voir plus)cross a wide array of deep learning applications. Despite their high computational demand, these models are extensively utilized for their superior sample quality and robust mode coverage. While research in diffusion generative models is advancing, exploration within the domain of computational pathology and its large-scale datasets has been comparatively gradual. Bridging the gap between the high-quality generation capabilities of Diffusion Generative Models and the intricate nature of pathology data, this paper presents an in-depth comparative analysis of diffusion methods applied to a pathology dataset. Our analysis extends to datasets with varying Fields of View (FOV), revealing that DGMs are highly effective in producing high-quality synthetic data. An ablative study is also conducted, followed by a detailed discussion on the impact of various methods on the synthesized histopathology images. One striking observation from our experiments is how the adjustment of image size during data generation can simulate varying fields of view. These findings underscore the potential of DGMs to enhance the quality and diversity of synthetic pathology data, especially when used with real data, ultimately increasing accuracy of deep learning models in histopathology. Code is available from https://github.com/AtlasAnalyticsLab/Diffusion4Path
2024-11-23
ArXiv (prépublication)
doi.org
arxiv.org