Portrait de Tal Arbel

Tal Arbel

Membre académique principal
arbeltal@mila.quebec
Chaire en IA Canada-CIFAR
Professeure titulaire, McGill University, Département de génie électrique et informatique
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage profond
Causalité
Modèles génératifs
Modèles probabilistes
Vision par ordinateur
Site web
Google Scholar
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Biographie

Tal Arbel est professeure titulaire au Département de génie électrique et informatique de l’Université McGill, où elle dirige le groupe de vision probabiliste et le laboratoire d'imagerie médicale du Centre sur les machines intelligentes.

Elle est titulaire d'une chaire en IA Canada-CIFAR et membre associée de Mila – Institut québécois d’intelligence artificielle ainsi que du Centre de recherche sur le cancer Goodman. Les recherches de la professeure Arbel portent sur le développement de méthodes probabilistes d'apprentissage profond dans les domaines de la vision par ordinateur et de l’analyse d'imagerie médicale pour un large éventail d'applications dans le monde réel, avec un accent particulier sur les maladies neurologiques.

Elle a remporté le prix de la recherche Christophe Pierre 2019 de McGill Engineering et est Fellow à l'Académie canadienne d'ingénierie. Elle fait régulièrement partie de l'équipe organisatrice de grandes conférences internationales sur la vision par ordinateur et l'analyse d'imagerie médicale (par exemple celles de la Medical Image Computing and Computer-Assisted Intervention Society/MICCAI et de Medical Imaging with Deep Learning/MIDL, l’International Conference on Computer Vision/ICCV ou encore la Conference on Computer Vision and Pattern Recognition/CVPR). Elle est rédactrice en chef et cofondatrice de la revue Machine Learning for Biomedical Imaging (MELBA).

Étudiants actuels

Publications

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses
Emily Kaczmarek
Justin Szeto
Brennan Nichyporuk
Tal Arbel
3D structural Magnetic Resonance Imaging (MRI) brain scans are commonly acquired in clinical settings to monitor a wide range of neurologica… (voir plus)l conditions, including neurodegenerative disorders and stroke. While deep learning models have shown promising results analyzing 3D MRI across a number of brain imaging tasks, most are highly tailored for specific tasks with limited labeled data, and are not able to generalize across tasks and/or populations. The development of self-supervised learning (SSL) has enabled the creation of large medical foundation models that leverage diverse, unlabeled datasets ranging from healthy to diseased data, showing significant success in 2D medical imaging applications. However, even the very few foundation models for 3D brain MRI that have been developed remain limited in resolution, scope, or accessibility. In this work, we present a general, high-resolution SimCLR-based SSL foundation model for 3D brain structural MRI, pre-trained on 18,759 patients (44,958 scans) from 11 publicly available datasets spanning diverse neurological diseases. We compare our model to Masked Autoencoders (MAE), as well as two supervised baselines, on four diverse downstream prediction tasks in both in-distribution and out-of-distribution settings. Our fine-tuned SimCLR model outperforms all other models across all tasks. Notably, our model still achieves superior performance when fine-tuned using only 20% of labeled training samples for predicting Alzheimer's disease. We use publicly available code and data, and release our trained model at https://github.com/emilykaczmarek/3D-Neuro-SimCLR, contributing a broadly applicable and accessible foundation model for clinical brain MRI analysis.
2025-09-11
ArXiv (prépublication)
doi.org
arxiv.org
SSL-AD: Spatiotemporal Self-Supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets
Emily Kaczmarek
Justin Szeto
Brennan Nichyporuk
Tal Arbel
Alzheimer's disease is a progressive, neurodegenerative disorder that causes memory loss and cognitive decline. While there has been extensi… (voir plus)ve research in applying deep learning models to Alzheimer's prediction tasks, these models remain limited by lack of available labeled data, poor generalization across datasets, and inflexibility to varying numbers of input scans and time intervals between scans. In this study, we adapt three state-of-the-art temporal self-supervised learning (SSL) approaches for 3D brain MRI analysis, and add novel extensions designed to handle variable-length inputs and learn robust spatial features. We aggregate four publicly available datasets comprising 3,161 patients for pre-training, and show the performance of our model across multiple Alzheimer's prediction tasks including diagnosis classification, conversion detection, and future conversion prediction. Importantly, our SSL model implemented with temporal order prediction and contrastive learning outperforms supervised learning on six out of seven downstream tasks. It demonstrates adaptability and generalizability across tasks and number of input images with varying time intervals, highlighting its capacity for robust performance across clinical applications. We release our code and model publicly at https://github.com/emilykaczmarek/SSL-AD.
2025-09-11
ArXiv (prépublication)
doi.org
arxiv.org
Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
Gian Mario Favero
Ge Ya Luo
Nima Fathi
Justin Szeto
Douglas Arnold
Brennan Nichyporuk
Christopher Pal
Tal Arbel
2025-08-08
ArXiv (prépublication)
doi.org
arxiv.org
AURA: A Multi-Modal Medical Agent for Understanding, Reasoning&Annotation
Nima Fathi
Amar Kumar
Tal Arbel
Recent advancements in Large Language Models (LLMs) have catalyzed a paradigm shift from static prediction systems to agentic AI agents capa… (voir plus)ble of reasoning, interacting with tools, and adapting to complex tasks. While LLM-based agentic systems have shown promise across many domains, their application to medical imaging remains in its infancy. In this work, we introduce AURA, the first visual linguistic explainability agent designed specifically for comprehensive analysis, explanation, and evaluation of medical images. By enabling dynamic interactions, contextual explanations, and hypothesis testing, AURA represents a significant advancement toward more transparent, adaptable, and clinically aligned AI systems. We highlight the promise of agentic AI in transforming medical image analysis from static predictions to interactive decision support. Leveraging Qwen-32B, an LLM-based architecture, AURA integrates a modular toolbox comprising: (i) a segmentation suite with phase grounding, pathology segmentation, and anatomy segmentation to localize clinically meaningful regions; (ii) a counterfactual image-generation module that supports reasoning through image-level explanations; and (iii) a set of evaluation tools including pixel-wise difference-map analysis, classification, and advanced state-of-the-art components to assess diagnostic relevance and visual interpretability.
2025-07-21
ArXiv (prépublication)
doi.org
arxiv.org
Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images
Zahra Tehrani Nasab
Hujun Ni
Amar Kumar
Tal Arbel
2025-07-16
ArXiv (prépublication)
doi.org
arxiv.org
Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models
Anita Kriz
Elizabeth Laura Janes
Xing Shen
Tal Arbel
Multimodal large language models (MLLMs) hold considerable promise for applications in healthcare. However, their deployment in safety-criti… (voir plus)cal settings is hindered by two key limitations: (i) sensitivity to prompt design, and (ii) a tendency to generate incorrect responses with high confidence. As clinicians may rely on a model's stated confidence to gauge the reliability of its predictions, it is especially important that when a model expresses high confidence, it is also highly accurate. We introduce Prompt4Trust, the first reinforcement learning (RL) framework for prompt augmentation targeting confidence calibration in MLLMs. A lightweight LLM is trained to produce context-aware auxiliary prompts that guide a downstream task MLLM to generate responses in which the expressed confidence more accurately reflects predictive accuracy. Unlike conventional calibration techniques, Prompt4Trust specifically prioritizes aspects of calibration most critical for safe and trustworthy clinical decision-making. Beyond improvements driven by this clinically motivated calibration objective, our proposed method also improves task accuracy, achieving state-of-the-art medical visual question answering (VQA) performance on the PMC-VQA benchmark, which is composed of multiple-choice questions spanning diverse medical imaging modalities. Moreover, our framework trained with a small downstream task MLLM showed promising zero-shot generalization to larger MLLMs in our experiments, suggesting the potential for scalable calibration without the associated computational costs. This work demonstrates the potential of automated yet human-aligned prompt engineering for improving the the trustworthiness of MLLMs in safety critical settings. Our codebase can be found at https://github.com/xingbpshen/prompt4trust.
2025-07-11
ArXiv (prépublication)
doi.org
arxiv.org
Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation
Zahra Tehrani Nasab
Amar Kumar
Tal Arbel
2025-06-10
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)
doi.org
arxiv.org
Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging
Amar Kumar
Anita Kriz
Barak Pertzov
Tal Arbel
2025-06-10
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)
doi.org
arxiv.org
The role of AI for MRI-analysis in multiple sclerosis—A brief overview
Jean-Pierre R. Falet
Steven Nobile
Aliya Szpindel
Berardino Barile
Amar Kumar
Joshua D. Durso-Finley
Tal Arbel
Douglas Arnold
2025-04-07
Frontiers Artif. Intell. (publié)
doi.org
PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar
Anita Kriz
Mohammad Havaei
Tal Arbel
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (voir plus)ta imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures robust to the unique complexities posed by medical imaging data. The rapid advancements in vision-language foundation models within the natural image domain prompt the question of how they can be adapted for medical imaging tasks. In this work, we present PRISM, a framework that leverages foundation models to generate high-resolution, language-guided medical image counterfactuals using Stable Diffusion. Our approach demonstrates unprecedented precision in selectively modifying spurious correlations (the medical devices) and disease features, enabling the removal and addition of specific attributes while preserving other image characteristics. Through extensive evaluation, we show how PRISM advances counterfactual generation and enables the development of more robust downstream classifiers for clinically deployable solutions. To facilitate broader adoption and research, we make our code publicly available at https://github.com/Amarkr1/PRISM.
2025-03-26
MIDL.io/2025/Conference (présentation orale)
doi.org
openreview.net
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
Parham Saremi
Amar Kumar
Mohammed Mohammed
Zahra Tehrani Nasab
Tal Arbel
2025-03-19
ArXiv (prépublication)
doi.org
arxiv.org
HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
Daniel Duenias
Brennan Nichyporuk
Tal Arbel
Tammy Riklin Raviv
The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Recor… (voir plus)ds (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a comprehensive understanding of the clinical condition of a patient, improving diagnosis and treatment decision. Deep Neural Networks (DNNs) consistently demonstrate outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex and multi-class Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI tabular data fusion methods. A link to our code can be found at https://github.com/daniel4725/HyperFusion
2025-02-20
Medical Image Analysis (inconnu)
doi.org
arxiv.org