A Comprehensive Overview on Data Augmentation Techniques for Medical Images

A significant number of recent advancements in Deep Learning have significantly benefited from training sets that are both larger and more diversified. Nevertheless, the collection of huge datasets for medical imaging continues to be a challenge due to issues around privacy and the expenses associated with labelling. Through the use of data augmentation, it is feasible to significantly increase the quantity and variety of data that is accessible for training purposes without actually collecting additional samples. Data augmentation techniques span from straightforward changes like cropping, padding, and flipping to more complicated generative models. These transformations are surprisingly powerful despite their apparent simplicity. Different data augmentation procedures are likely to function differently depending on the nature of the input and the visual task that is being performed. As a result of this, it is probable that medical imaging calls for particular augmentation algorithms that are capable of producing believable data samples and enabling the successful regularization of deep neural networks. This paper reviews different data augmentation techniques.