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Current data augmentation techniques and transformations are well suited forimproving the size and quality of natural image datasets but are not yetoptimized for medical imaging. We hypothesize that sub-optimal dataaugmentations can easily distort or occlude medical images, leading to falsepositives or negatives during patient diagnosis, prediction, or therapy/surgeryevaluation. In our experimental results, we found that utilizing commonly usedintensity-based data augmentation distorts the MRI scans and leads to textureinformation loss, thus negatively affecting the overall performance ofclassification. Additionally, we observed that commonly used data augmentationmethods cannot be used with a plug-and-play approach in medical imaging, andrequires manual tuning and adjustment.

A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications