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

Collaborateur·rice de recherche - UdeM

Nima Fathi

Maîtrise recherche - McGill

Gian Favero

Maîtrise recherche - McGill

Elizabeth Laura Janes

Maîtrise recherche - McGill

Emily Kaczmarek

Doctorat - McGill

Doctorat - McGill

Mohamed Mohamed

Maîtrise recherche - McGill

ALEXANDER NATALIE

Collaborateur·rice de recherche - N/A

Toky Raharison Ralambomihanta

Baccalauréat - McGill

Ryan Rezai

Maîtrise recherche - McGill

Parham Saremi

Maîtrise recherche - McGill

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

Xing Shen

Maîtrise recherche - McGill

Maîtrise recherche - McGill

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

Lire l'article

Publications

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

Zahra Tehrani Nasab

Hujun Ni

2025-07-01

arXiv (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

Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image ana… (voir plus)lysis. However, safe deployment of these models into real-world clinical practice requires an in-depth analysis of the accuracies of their predictions, and their associated calibration errors, particularly across different demographic subgroups. In this work, we present the first investigation into the calibration biases and demographic unfairness of MLLMs'predictions and confidence scores in few-shot in-context learning for medical image classification. We introduce CALIN, an inference-time calibration method designed to mitigate the associated biases. Specifically, CALIN estimates the amount of calibration needed, represented by calibration matrices, using a bi-level procedure: progressing from the population level to the subgroup level prior to inference. It then applies this estimation to calibrate the predicted confidence scores during inference. Experimental results on three medical imaging datasets: PAPILA for fundus image classification, HAM10000 for skin cancer classification, and MIMIC-CXR for chest X-ray classification demonstrate CALIN's effectiveness at ensuring fair confidence calibration in its prediction, while improving its overall prediction accuracies and exhibiting minimum fairness-utility trade-off. Our codebase can be found at https://github.com/xingbpshen/medical-calibration-fairness-mllm.

2025-06-29

ArXiv (prépublication)

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

Zahra Tehrani Nasab

2025-06-11

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

2025-06-01

arXiv (publié)

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

Zahra TehraniNasab

2025-03-30

ArXiv (prépublication)

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

Zahra TehraniNasab

Text-to-image diffusion models have demonstrated a remarkable ability to generate photorealistic images from natural language prompts. These… (voir plus) high-resolution, language-guided synthesized images are essential for the explainability of disease or exploring causal relationships. However, their potential for disentangling and controlling latent factors of variation in specialized domains like medical imaging remains under-explored. In this work, we present the first investigation of the power of pre-trained vision-language foundation models, once fine-tuned on medical image datasets, to perform latent disentanglement for factorized medical image generation and interpolation. Through extensive experiments on chest X-ray and skin datasets, we illustrate that fine-tuned, language-guided Stable Diffusion inherently learns to factorize key attributes for image generation, such as the patient's anatomical structures or disease diagnostic features. We devise a framework to identify, isolate, and manipulate key attributes through latent space trajectory traversal of generative models, facilitating precise control over medical image synthesis.

2025-03-30

ArXiv (prépublication)

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

Anita Kriz

B. Pertzov

2025-03-30

ArXiv (prépublication)

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

Anita Kriz

B. Pertzov

Vision-language foundation models (VLMs) have shown impressive performance in guiding image generation through text, with emerging applicati… (voir plus)ons in medical imaging. In this work, we are the first to investigate the question: 'Can fine-tuned foundation models help identify critical, and possibly unknown, data properties?' By evaluating our proposed method on a chest x-ray dataset, we show that these models can generate high-resolution, precisely edited images compared to methods that rely on Structural Causal Models (SCMs) according to numerous metrics. For the first time, we demonstrate that fine-tuned VLMs can reveal hidden data relationships that were previously obscured due to available metadata granularity and model capacity limitations. Our experiments demonstrate both the potential of these models to reveal underlying dataset properties while also exposing the limitations of fine-tuned VLMs for accurate image editing and susceptibility to biases and spurious correlations.

2025-03-30

ArXiv (prépublication)

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

Gian Mario Favero

2025-03-27

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.

2025-03-27

MIDL.io/2025/Conference (présentation orale)