  Current data augmentation techniques and transformations are well suited for
improving the size and quality of natural image datasets but are not yet
optimized for medical imaging. We hypothesize that sub-optimal data
augmentations can easily distort or occlude medical images, leading to false
positives or negatives during patient diagnosis, prediction, or therapy/surgery
evaluation. In our experimental results, we found that utilizing commonly used
intensity-based data augmentation distorts the MRI scans and leads to texture
information loss, thus negatively affecting the overall performance of
classification. Additionally, we observed that commonly used data augmentation
methods cannot be used with a plug-and-play approach in medical imaging, and
requires manual tuning and adjustment.
