Tal Arbel

Biography

Tal Arbel is a professor in the Department of Electrical and Computer Engineering at McGill University, where she is the director of the Probabilistic Vision Group and Medical Imaging Lab in the Centre for Intelligent Machines.

She is also a Canada CIFAR AI Chair, an associate academic member of Mila – Quebec Artificial Intelligence Institute and an associate member of the Goodman Cancer Research Centre.

Arbel’s research focuses on the development of probabilistic deep learning methods in computer vision and medical image analysis for a wide range of real-world applications, with a focus on neurological diseases.

She is a recipient of the 2019 McGill Engineering Christophe Pierre Research Award and a Fellow of the Canadian Academy of Engineering. She regularly serves on the organizing team of major international conferences in computer vision and in medical image analysis (e.g. MICCAI, MIDL, ICCV, CVPR). She is currently the Editor-in-Chief and co-founder of the arXiv overlay journal: Machine Learning for Biomedical Imaging (MELBA).

Current Students

Karl Bridi

Master's Research - McGill University

karl.bridi@mila.quebec

Kailin Chu

Master's Research - McGill University

kailin.chu@mila.quebec

sebastian.clancy@mila.quebec

Sebastian Chiara Clancy

PhD - McGill University

cornelius.crijnen@mila.quebec

Cornelius Crijnen

PhD - McGill University

Website

oscar.cruzhernandez@mila.quebec

Oscar Cruzhernandez

Research Intern - McGill University

Charbel El Feghali

Master's Research - McGill University

charbel.elfeghali@mila.quebec

Carlotta Hoelzle

PhD - McGill University

carlotta.hoelzle@mila.quebec

elizabeth.janes@mila.quebec

Elizabeth Laura Janes

PhD - McGill University

Emily Kaczmarek

PhD - McGill University

emily.kaczmarek@mail.mcgill.ca

Yusong Li

PhD - McGill University University

yusong.li@mila.quebec

Toky Raharison Ralambomihanta

Yik Yu Ng

Research Intern - McGill University

yik.ng@mila.quebec

Master's Research - McGill University

tokiniaina.raharisonralambomihan@mail.mcgill.ca

Ryan Rezai

Master's Research - McGill University

ryan.rezai@mila.quebec

Rachel Ruddy

Research Intern - McGill University

rachel.ruddy@mila.quebec

PRISM: An Explainable Generative AI Model for Medical Imaging

Minh To

Collaborating researcher - UBC

Blog Posts

Image of an Xray and the DDIM process to generate counterfactual version of Xrays

July 1, 2025

Amar Kumar

Anita Kriz

Mohammed Havaei

Tal Arbel

Read the article

Publications

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses

3D structural Magnetic Resonance Imaging (MRI) brain scans are commonly acquired in clinical settings to monitor a wide range of neurologica… (see more)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 (preprint)

SSL-AD: Spatiotemporal Self-Supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets

Alzheimer's disease is a progressive, neurodegenerative disorder that causes memory loss and cognitive decline. While there has been extensi… (see more)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 (preprint)

Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments

Gian Mario Favero

Ge Ya Luo

Douglas Arnold

Christopher Pal

2025-08-08

ArXiv (preprint)

AURA: A Multi-Modal Medical Agent for Understanding, Reasoning&Annotation

Nima Fathi

Recent advancements in Large Language Models (LLMs) have catalyzed a paradigm shift from static prediction systems to agentic AI agents capa… (see more)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 (preprint)

Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images

Zahra Tehrani Nasab

Hujun Ni

2025-07-16

ArXiv (preprint)

Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models

Anita Kriz

Elizabeth Laura Janes

Xing Shen

Multimodal large language models (MLLMs) hold considerable promise for applications in healthcare. However, their deployment in safety-criti… (see more)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 (preprint)

Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation

Zahra Tehrani Nasab

2025-06-10

2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (published)

Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging

Anita Kriz

Barak Pertzov

2025-06-10

2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (published)

The role of AI for MRI-analysis in multiple sclerosis—A brief overview

Jean-Pierre R. Falet

Steven Nobile

Aliya Szpindel

Berardino Barile

Joshua D. Durso-Finley

Douglas Arnold

2025-04-07

Frontiers Artif. Intell. (published)

PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion

Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, da… (see more)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 (oral)

openreview.net

RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models

Parham Saremi

Mohammed Mohammed

Zahra Tehrani Nasab

2025-03-19

ArXiv (preprint)

HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling

Daniel Duenias

Brennan Nichyporuk

Tammy Riklin Raviv

The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Recor… (see more)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 (unknown)