Portrait de Zahra TehraniNasab

Zahra TehraniNasab

Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
Tal Arbel
Sujets de recherche
Apprentissage automatique médical
Apprentissage profond
Modèles génératifs
Vision par ordinateur

Publications

Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images
Zahra TehraniNasab
Amar Kumar
Tal Arbel
Medical image synthesis presents unique challenges due to the inherent complexity and high-resolution details required in clinical contexts.… (voir plus) Traditional generative architectures such as Generative Adversarial Networks (GANs) or Variational Auto Encoder (VAEs) have shown great promise for high-resolution image generation but struggle with preserving fine-grained details that are key for accurate diagnosis. To address this issue, we introduce Pixel Perfect MegaMed, the first vision-language foundation model to synthesize images at resolutions of 1024x1024. Our method deploys a multi-scale transformer architecture designed specifically for ultra-high resolution medical image generation, enabling the preservation of both global anatomical context and local image-level details. By leveraging vision-language alignment techniques tailored to medical terminology and imaging modalities, Pixel Perfect MegaMed bridges the gap between textual descriptions and visual representations at unprecedented resolution levels. We apply our model to the CheXpert dataset and demonstrate its ability to generate clinically faithful chest X-rays from text prompts. Beyond visual quality, these high-resolution synthetic images prove valuable for downstream tasks such as classification, showing measurable performance gains when used for data augmentation, particularly in low-data regimes. Our code is accessible through the project website - https://tehraninasab.github.io/pixelperfect-megamed.
2025-07-17
ArXiv (prépublication)
arxiv.org
arxiv.org
Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation
Zahra TehraniNasab
Amar Kumar
Tal Arbel
2025-03-30
ArXiv (prépublication)
arxiv.org
arxiv.org
Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation
Zahra TehraniNasab
Amar Kumar
Tal Arbel
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)
doi.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 TehraniNasab
Tal Arbel
2025-03-20
ArXiv (prépublication)
arxiv.org
arxiv.org