Vertical Integration of Data Augmentation Using Generative AI in Medical Imagining for Population Health: Deep Learning Approaches for Transforming Health 5.0

Generative artificial intelligence (AI) and deep learning techniques has shown promising results in the field of medical imaging, mainly for enhancing population health outcomes. The utilization of deep learning has gained popularity in medical image analysis as a significant hurdle persists due to the restricted accessibility of training data especially within the medical domain where data acquisition proves costly and is bound by privacy regulations. In tackling this challenge, data augmentation methods present a remedy by artificially expanding the pool of training samples. Many of these modalities are characterized by their highly-dimensional data nature and the medical domain often faces constraints in terms of the number of available training samples, particularly when dealing with rare diseases. Given that deep learning algorithms typically require extensive datasets, executing such applications with limited sample sizes can be exceptionally challenging. To address this challenge, data augmentation emerges as a solution to enlarge the training set artificially by generating new samples. This technique is widely adopted in computer vision and has become integral to deep learning applications when abundant training datasets are not at disposal. This chapter explores the potential of deep learning approaches in transforming healthcare practices under the framework of Health 5.0.