A diffusion model for universal medical image enhancement

• Published in: Communications medicine Vol. 5; no. 1; pp. 294 - 14
• Main Authors: Fei, Ben, Li, Yixuan, Yang, Weidong, Gao, Hengjun, Xu, Jingyi, Ma, Lipeng, Yang, Yatian, Zhou, Pinghong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.07.2025; Springer Nature B.V; Nature Portfolio
• Subjects: 631/114/116/1925; 692/700/1421/2109; Datasets; Diffusion models; Endoscopy; Ethics; Medical imaging; Medicine; Medicine & Public Health; Normal distribution; Ultrasonic imaging
• DOI: 10.1038/s43856-025-00998-1
• ISSN: 2730-664X

Background The development of medical imaging techniques has made a significant contribution to clinical decision-making. However, the existence of suboptimal imaging quality, as indicated by irregular illumination or imbalanced intensity, presents significant obstacles in automating disease screening, analysis, and diagnosis. Existing approaches for natural image enhancement are mostly trained with numerous paired images, presenting challenges in data collection and training costs, all while lacking the ability to generalize effectively. Methods Here, we introduce a pioneering training-free Diffusion Model for Universal Medical Image Enhancement, named UniMIE. UniMIE demonstrates its unsupervised enhancement capabilities across various medical image modalities without the need for any fine-tuning. It accomplishes this by relying solely on a single pre-trained model from ImageNet. Results We conduct a comprehensive evaluation on 13 imaging modalities and over 15 medical types, demonstrating better qualities, robustness, and accuracy than other modality-specific and data-inefficient models. By delivering high-quality enhancement and corresponding accuracy downstream tasks across a wide range of tasks, UniMIE exhibits considerable potential to accelerate the advancement of diagnostic tools and customized treatment plans. Conclusions UniMIE represents a transformative approach to medical image enhancement, offering a versatile and robust solution that adapts to diverse imaging conditions. By improving image quality and facilitating better downstream analyses, UniMIE has the potential to revolutionize clinical workflows and enhance diagnostic accuracy across a wide range of medical applications. Plain language summary Medical images, such as microscopy and X-rays, are important tools for diagnosing and treating illnesses. However, these images often have poor quality due to issues that include low contrast or noise, making it harder for doctors to use them effectively. To address this issue, we developed a method called UniMIE, which improves the quality of medical images without requiring computational training or inclusion of additional medical data. UniMIE enhances image clarity and brightness, enabling doctors to more easily analyze and diagnose conditions. Experimental results demonstrate that UniMIE outperforms existing enhancement methods across diverse applications, including COVID-19 detection, brain imaging and cardiac imaging. We hope that this approach will contribute to improved diagnostic accuracy and, ultimately, better patient outcomes. Fei, Li, Yang, Gao et al. present UniMIE, a training-free diffusion model for universal medical image enhancement without fine-tuning. It achieves high-quality results across 13 modalities and 15+ medical types using one ImageNet pre-trained model.