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In the realm of medical imaging, the training of machine learning modelsnecessitates a large and varied training dataset to ensure robustness andinteroperability. However, acquiring such diverse and heterogeneous data can bedifficult due to the need for expert labeling of each image and privacyconcerns associated with medical data. To circumvent these challenges, dataaugmentation has emerged as a promising and cost-effective technique forincreasing the size and diversity of the training dataset. In this study, weprovide a comprehensive review of the specific data augmentation techniquesemployed in medical imaging and explore their benefits. We conducted anin-depth study of all data augmentation techniques used in medical imaging,identifying 11 different purposes and collecting 65 distinct techniques. Thetechniques were operationalized into spatial transformation-based, color andcontrast adjustment-based, noise-based, deformation-based, data mixing-based,filters and mask-based, division-based, multi-scale and multi-view-based, andmeta-learning-based categories. We observed that some techniques require manualspecification of all parameters, while others rely on automation to adjust thetype and magnitude of augmentation based on task requirements. The utilizationof these techniques enables the development of more robust models that can beapplied in domains with limited or challenging data availability. It isexpected that the list of available techniques will expand in the future,providing researchers with additional options to consider.

Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis