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eess.IV2025

Imagining Alternatives: Towards High-Resolution 3D Counterfactual Medical Image Generation via Language Guidance

Mohamed Mohamed, Brennan Nichyporuk, Douglas L. Arnold +1

Vision-language models have demonstrated impressive capabilities in generating 2D images under various conditions; however, the success of these models is largely enabled by extens…

eess.IV2025

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…

eess.IV2025

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…

eess.IV2025

Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments

Gian Mario Favero, Ge Ya Luo, Nima Fathi +5

Image-based personalized medicine has the potential to transform healthcare, particularly for diseases that exhibit heterogeneous progression such as Multiple Sclerosis (MS). In th…

eess.IV2025

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 +2

Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image analysis. However, safe deployment of these…