Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

Abstract

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.

A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data

“…To date, using generative models for synthetic data augmentation on limited data is an under-explored research area. Although generative models are commonly used for larger datasets in medical imaging with a reported increase in performance [9][10][11][12][13], we do not see the same rigorous research towards scarce data scenarios, where such approaches would be most helpful. We aim to close this gap and initiate the discussion in this area.…”

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration

“…Data-augmentation has been reported as a valuable technique to compensate for e cient learning in AI training. 11,12 It has the advantage of increasing the amount of training data by creating new images from the original images. AI training with data-augmentation has been reported as a useful technique for creating AI systems that detect Barrett's esophagus and colorectal polyps in gastroenterology.…”

“…In recent years, data-augmentation with various techniques has been reported as a valuable method for generating many image datasets from a small image dataset for training AI. 11,12 Data-augmentation may provide a solution to guarantee data comprehensiveness. However, to our knowledge, no study has compared the effectiveness of various data-augmentation techniques for optimal image datasets of AI training in the gastroenterological or pathological elds.…”

Principles of artificial intelligence and its application in cardiovascular medicine

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