Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images

TL;DR

Pixel Perfect MegaMed is a novel high-resolution medical image synthesis model that uses a multi-scale transformer architecture and vision-language alignment to generate detailed, clinically faithful images from text prompts, improving downstream diagnostic tasks.

Contribution

It introduces the first vision-language foundation model capable of synthesizing 1024x1024 medical images with fine details, combining multi-scale transformers and medical-specific alignment techniques.

Findings

Successfully generates high-resolution chest X-rays from text prompts.

Enhances downstream classification performance through data augmentation.

Achieves detailed, clinically faithful image synthesis at unprecedented resolution.

Abstract

Medical image synthesis presents unique challenges due to the inherent complexity and high-resolution details required in clinical contexts. 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.