A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications
- University of Central Florida（中佛罗里达大学）
- Northwestern University（西北大学）
- Mayo Clinic（梅奥诊所）
- Sheikh Shakhbout Medical City（谢赫·沙赫布特医疗城）
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英文摘要：
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.
