2 citations · 3 across the 11 of their papers we have counts for
11 papers
Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation
Bailey Trang, Parham Saremi, Alan Q. Wang +6
Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can lead to multiple plausible outputs. To ge…
Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images
Zahra TehraniNasab, Hujun Ni, Amar Kumar +1
Medical image synthesis presents unique challenges due to the inherent complexity and high-resolution details required in clinical contexts. Traditional generative architectures su…
Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging
Zahra TehraniNasab, Amar Kumar, Tal Arbel
Advancements in diffusion-based foundation models have improved text-to-image generation, yet most efforts have been limited to low-resolution settings. As high-resolution image sy…
AURA: A Multi-Modal Medical Agent for Understanding, Reasoning & Annotation
Nima Fathi, Amar Kumar, Tal Arbel
Recent advancements in Large Language Models (LLMs) have catalyzed a paradigm shift from static prediction systems to agentic AI agents capable of reasoning, interacting with tools…
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 high-resolution, language-guided synthe…
Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging
Amar Kumar, Anita Kriz, Barak Pertzov +1
Vision-language foundation models (VLMs) have shown impressive performance in guiding image generation through text, with emerging applications in medical imaging. In this work, we…
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
Parham Saremi, Amar Kumar, Mohamed Mohamed +2
Vision-Language Foundation Models (VLFM) have shown a tremendous increase in performance in terms of generating high-resolution, photorealistic natural images. While VLFMs show a r…
PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion
Amar Kumar, Anita Kriz, Mohammad Havaei +1
Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, data imbalances, and limited text annotati…
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Nima Fathi, Amar Kumar, Brennan Nichyporuk +2
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor g…
Debiasing Counterfactuals In the Presence of Spurious Correlations
Amar Kumar, Nima Fathi, Raghav Mehta +4
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correlations (i.e. confounders), should they b…
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
Amar Kumar, Anjun Hu, Brennan Nichyporuk +4
The discovery of patient-specific imaging markers that are predictive of future disease outcomes can help us better understand individual-level heterogeneity of disease evolution.…