Portrait de Parham Saremi

Parham Saremi

Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
Tal Arbel
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage profond
Modèles de diffusion
Modèles génératifs
Modèles probabilistes
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation
Bailey Trang
Parham Saremi
Alan Q. Wang
Fangrui Huang
Zahra TehraniNasab
Amar Kumar
Tal Arbel
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)
doi.org
openreview.net
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Gian Mario Favero
Parham Saremi
Emily Kaczmarek
Brennan Nichyporuk
Tal Arbel
2025-03-27
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 TehraniNasab
Tal Arbel
2025-03-20
ArXiv (prépublication)
arxiv.org
arxiv.org
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
2025-03-01
arXiv (publié)
doi.org
arxiv.org
Conditional Diffusion Models are Medical Image Classifiers that Provide Explainability and Uncertainty for Free
Gian Mario Favero
Parham Saremi
Emily Kaczmarek
Brennan Nichyporuk
Tal Arbel
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)
arxiv.org
arxiv.org