Deep Learning Approaches for Data Augmentation in Medical Imaging: A
  Review
Aghiles Kebaili Jérôme Lapuyade-Lahorgue Su Ruan
Deep learning has become a popular tool for medical image analysis, but the
limited availability of training data remains a major challenge, particularly
in the medical field where data acquisition can be costly and subject to
privacy regulations. Data augmentation techniques offer a solution by
artificially increasing the number of training samples, but these techniques
often produce limited and unconvincing results. To address this issue, a
growing number of studies have proposed the use of deep generative models to
generate more realistic and diverse data that conform to the true distribution
of the data. In this review, we focus on three types of deep generative models
for medical image augmentation: variational autoencoders, generative
adversarial networks, and diffusion models. We provide an overview of the
current state of the art in each of these models and discuss their potential
for use in different downstream tasks in medical imaging, including
classification, segmentation, and cross-modal translation. We also evaluate the
strengths and limitations of each model and suggest directions for future
research in this field. Our goal is to provide a comprehensive review about the
use of deep generative models for medical image augmentation and to highlight
the potential of these models for improving the performance of deep learning
algorithms in medical image analysis.