Tal Arbel

2025-03-30

ArXiv (prépublication)

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

Zahra Tehraninasab

Amar Kumar

2025-03-30

ArXiv (prépublication)

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

Amar Kumar

Anita Kriz

B. Pertzov

2025-03-30

ArXiv (prépublication)

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

Amar Kumar

Anita Kriz

B. Pertzov

2025-03-30

ArXiv (prépublication)

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

Gian Mario Favero

Parham Saremi

Emily Kaczmarek

Brennan Nichyporuk

2025-03-27

MIDL.io/2025/Conference (présentation orale)

openreview.net

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

Amar Kumar

Anita Kriz

Mohammad Havaei

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)

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 Tehraninasab

2025-03-20

ArXiv (prépublication)

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

Amar Kumar

Anita Kriz

Mohammad Havaei

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.

2025-03-01

arXiv (publié)

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

Parham Saremi

Amar Kumar

Mohammed Mohammed

Zahra Tehraninasab

2025-03-01

arXiv (publié)

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

Amar Kumar

Anita Kriz

Mohammad Havaei

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.

2025-02-28

ArXiv (prépublication)

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

Gian Mario Favero

Parham Saremi

Emily Kaczmarek

Brennan Nichyporuk

Discriminative classifiers have become a foundational tool in deep learning for medical imaging, excelling at learning separable features of… (voir plus) complex data distributions. However, these models often need careful design, augmentation, and training techniques to ensure safe and reliable deployment. Recently, diffusion models have become synonymous with generative modeling in 2D. These models showcase robustness across a range of tasks including natural image classification, where classification is performed by comparing reconstruction errors across images generated for each possible conditioning input. This work presents the first exploration of the potential of class conditional diffusion models for 2D medical image classification. First, we develop a novel majority voting scheme shown to improve the performance of medical diffusion classifiers. Next, extensive experiments on the CheXpert and ISIC Melanoma skin cancer datasets demonstrate that foundation and trained-from-scratch diffusion models achieve competitive performance against SOTA discriminative classifiers without the need for explicit supervision. In addition, we show that diffusion classifiers are intrinsically explainable, and can be used to quantify the uncertainty of their predictions, increasing their trustworthiness and reliability in safety-critical, clinical contexts. Further information is available on our project page: https://faverogian.github.io/med-diffusion-classifier.github.io/

2025-02-06

ArXiv (prépublication)

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

Douglas Arnold

2025-01-01

Frontiers Artif. Intell. (publié)