Review of diffusion models and its applications in biomedical informatics

Abstract

The diffusion model, a cutting-edge deep generative technique, is gaining traction in biomedical informatics, showcasing promising applications across various domains. This review presents an overview of the working principles, categories, and numerous applications of diffusion models in biomedical research. In medical imaging, these models, through frameworks like Denoising Diffusion Probabilistic Models (DDPMs) and Stochastic Differential Equations (SDE), offer advanced solutions for image generation, reconstruction, segmentation, and denoising. Notably, they’ve been employed in synthesizing 2D/3D medical images, MRI, and PET image reconstruction, and segmentation tasks such as labeled MRI generation. In the realm of structured Electronic Health Records (EHR) data, diffusion models excel in data synthesis, offering innovative approaches in the face of challenges like data privacy and data gaps. Furthermore, these models are proving pivotal in physiological signal domains, such as EEG and ECG, for signal generation and restoration amidst data loss and noise disruptions. Another significant application lies in the design and prediction of small molecules and protein structures. These models unveil profound insights into the vast molecular space, guiding endeavors in drug design, molecular docking, and antibody construction. Despite their potential, there are inherent limitations, emphasizing the need for further research, validation, interdisciplinary collaboration, and robust benchmarking to ensure practical reliability and efficiency. This review seeks to shed light on the profound capabilities and challenges of diffusion models in the rapidly evolving landscape of biomedical research.