Tal Arbel

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

Cornelius Crijnen

Doctorat - McGill

Elizabeth Laura Janes

Maîtrise recherche - McGill

Emily Kaczmarek

Doctorat - McGill

Toky Raharison Ralambomihanta

Baccalauréat - McGill

Ryan Rezai

Maîtrise recherche - McGill

Parham Saremi

Maîtrise recherche - McGill

Xing Shen

Maîtrise recherche - McGill

Maîtrise recherche - McGill

Minh To

Collaborateur·rice de recherche - UBC

PRISM : Un modèle d'IA générative explicable pour l'imagerie médicale

Billets de blogue

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

1 juillet 2025

par

Amar Kumar

Anita Kriz

Mohammed Havaei

Tal Arbel

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Publications

Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance

Mohamed Mohamed

Brennan Nichyporuk

Douglas Arnold

Vision-language models have demonstrated impressive capabilities in generating 2D images under various conditions; however the impressive pe… (voir plus)rformance of these models in 2D is largely enabled by extensive, readily available pretrained foundation models. Critically, comparable pretrained foundation models do not exist for 3D, significantly limiting progress in this domain. As a result, the potential of vision-language models to produce high-resolution 3D counterfactual medical images conditioned solely on natural language descriptions remains completely unexplored. Addressing this gap would enable powerful clinical and research applications, such as personalized counterfactual explanations, simulation of disease progression scenarios, and enhanced medical training by visualizing hypothetical medical conditions in realistic detail. Our work takes a meaningful step toward addressing this challenge by introducing a framework capable of generating high-resolution 3D counterfactual medical images of synthesized patients guided by free-form language prompts. We adapt state-of-the-art 3D diffusion models with enhancements from Simple Diffusion and incorporate augmented conditioning to improve text alignment and image quality. To our knowledge, this represents the first demonstration of a language-guided native-3D diffusion model applied specifically to neurological imaging data, where faithful three-dimensional modeling is essential to represent the brain's three-dimensional structure. Through results on two distinct neurological MRI datasets, our framework successfully simulates varying counterfactual lesion loads in Multiple Sclerosis (MS), and cognitive states in Alzheimer's disease, generating high-quality images while preserving subject fidelity in synthetically generated medical images. Our results lay the groundwork for prompt-driven disease progression analysis within 3D medical imaging.

2025-10-11

Lecture Notes in Computer Science (publié)

Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification

Xing Shen

Justin Szeto

Mingyang Li

Hengguan Huang

2025-09-19

Lecture Notes in Computer Science (publié)

Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation

Bailey Trang

Parham Saremi

Alan Q. Wang

Fangrui Huang

Zahra TehraniNasab

Li Fei-Fei

Ehsan Adeli

Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can l… (voir plus)ead to multiple plausible outputs. To generate diverse images reflecting this diversity, traditional methods often modify random seeds, making it difficult to discern meaningful differences between samples, or diversify the input prompt, which is limited in verbally interpretable diversity. We propose Rainbow, a novel conditional image generation framework, applicable to any pretrained conditional generative model, that addresses inherent condition/prompt uncertainty and generates diverse plausible images. Rainbow is based on a simple yet effective idea: decomposing the input condition into diverse latent representations, each capturing an aspect of the uncertainty and generating a distinct image. First, we integrate a latent graph, parameterized by Generative Flow Networks (GFlowNets), into the prompt representation computation. Second, leveraging GFlowNets' advanced graph sampling capabilities to capture uncertainty and output diverse trajectories over the graph, we produce multiple trajectories that collectively represent the input condition, leading to diverse condition representations and corresponding output images. Evaluations on natural image and medical image datasets demonstrate Rainbow's improvement in both diversity and fidelity across image synthesis, image generation, and counterfactual generation tasks.

2025-09-17

NeurIPS.cc/2025/Conference (poster)

openreview.net

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… (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)

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… (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)

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

2025-07-31

arXiv (publié)

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… (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)

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… (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.

2025-06-30

arXiv (publié)

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

Zahra Tehrani Nasab

Hujun Ni

2025-06-30

arXiv (publié)

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) (publié)

Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free

Gian Mario Favero

2025-03-26

MIDL.io/2025/Conference (présentation orale)

openreview.net

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… (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)