Trends and applications of variational autoencoders in medical imaging analysis

Abstract

Automated medical imaging analysis plays a crucial role in modern healthcare, with deep learning emerging as a widely adopted solution. However, traditional supervised learning methods often struggle to achieve optimal performance due to increasing challenges such as data scarcity and variability. In response, generative artificial intelligence has gained significant attention, particularly Variational Autoencoders (VAEs), which have been extensively utilized to address various challenges in medical imaging. This review analyzed 118 articles published in the Web of Science database between 2018 and 2024. Bibliometric analysis was conducted to map research trends, while a curated compilation of datasets and evaluation metrics were extracted to underscore the importance of standardization in deep learning workflows. VAEs have been applied across multiple healthcare applications, including anomaly detection, segmentation, classification, synthesis, registration, harmonization, and clustering. Findings suggest that VAE-based models are increasingly applied in medical imaging, with Magnetic Resonance Imaging emerging as the dominant modality and image synthesis as a primary application. The growing interest in this field highlights the potential of VAEs to enhance medical imaging analysis by overcoming existing limitations in data-driven healthcare solutions. This review serves as a valuable resource for researchers looking to integrate VAE models into healthcare applications, offering an overview of current advancements.