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.
@article{arxiv.2301.02181,
title = {A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications},
author = {Tara M. Pattilachan and Ugur Demir and Elif Keles and Debesh Jha and Derk Klatte and Megan Engels and Sanne Hoogenboom and Candice Bolan and Michael Wallace and Ulas Bagci},
journal= {arXiv preprint arXiv:2301.02181},
year = {2023}
}