Enhancing Medical Imaging Through Data Augmentation: A Review

This article conducts a comprehensive review of the existing literature on data augmentation and data generation techniques within the context of medical image processing. Addressing the challenges associated with building sizable medical image datasets, including the rarity of certain medical conditions, patient privacy concerns, the need for expert labeling, and the associated expenses, this review focuses on methodologies aimed at enhancing the volume and diversity of available data. Special emphasis is placed on techniques such as data augmentation and data generation, with a particular interest in their application to medical image datasets. The objective is to provide a synthesis of current research, methodologies, and advancements in this domain, offering insights into the state-of-the-art practices and identifying potential avenues for future developments in medical image data augmentation.