Is synthetic data generation effective in maintaining clinical biomarkers? Investigating diffusion models across diverse imaging modalities.

Abstract

Introduction: The integration of recent technologies in medical imaging has become a cornerstone of modern healthcare, facilitating detailed analysis of internal anatomy and pathology. Traditional methods, however, often grapple with data-sharing restrictions due to privacy concerns. Emerging techniques in artificial intelligence offer innovative solutions to overcome these constraints, with synthetic data generation enabling the creation of realistic medical imaging datasets, but the preservation of critical hidden medical biomarkers is an open question.

Methods: This study employs state-of-the-art Denoising Diffusion Probabilistic Models integrated with a Swin-transformer-based network to generate synthetic medical data. Three distinct areas of medical imaging - radiology, ophthalmology, and histopathology - are explored. The quality of synthetic images is evaluated through a classifier trained to identify the preservation of medical biomarkers.

Results: The diffusion model effectively preserves key medical features, such as lung markings and retinal abnormalities, producing synthetic images closely resembling real data. Classifier performance demonstrates the reliability of synthetic data for downstream tasks, with F1 and AUC reaching 0.8-0.99.

Discussion: This work provides valuable insights into the potential of diffusion-based models for generating realistic, biomarker-preserving synthetic images across various medical imaging modalities. These findings highlight the potential of synthetic data to address challenges such as data scarcity and privacy concerns in clinical practice, research, and education.