mlsanityData to 7 Oct 2026

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

arXiv 2507.12698 ↗First posted 17 Jul 2025eess.IV cs.CVcited by 0

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 18 of its 29 references that are in the map. First posted on arXiv 17 Jul 2025.

Builds on

18 of 29 references.

References · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  4. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  5. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  6. 2023MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generationcited by 529
  7. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 299
  8. 2023Efficient Diffusion Training via Min-SNR Weighting Strategycited by 242
  9. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  10. 2023DemoFusion: Democratising High-Resolution Image Generation With No cited by 105
  11. 2020GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learningcited by 50
  12. 2023RadEdit: stress-testing biomedical vision models via diffusion image editingcited by 28
6 more
  1. 2022CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGANcited by 25
  2. 2021Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentationcited by 20
  3. 2025PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusioncited by 17
  4. 2023Debiasing Counterfactuals In the Presence of Spurious Correlationscited by 12
  5. 2022Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markerscited by 10
  6. 2024DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanationscited by 6

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  1. 2024Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language Conditioning
  2. 2026CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
  3. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
  4. 2022Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
  5. 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation
  6. 2025A Generative Foundation Model for Chest Radiography
  7. 2026Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers
  8. 2024Deep Generative Models for 3D Medical Image Synthesis