Robust Training with Data Augmentation for Medical Imaging Classification
This work addresses reliability and trust issues in medical diagnostics for healthcare professionals, though it is incremental as it builds on existing adversarial training and data augmentation methods.
The authors tackled the vulnerability of deep neural networks in medical imaging classification to adversarial attacks and distribution shifts by proposing a robust training algorithm with data augmentation (RTDA), which achieved superior robustness and improved generalization while maintaining high clean accuracy across mammograms, X-rays, and ultrasound datasets.
Deep neural networks are increasingly being used to detect and diagnose medical conditions using medical imaging. Despite their utility, these models are highly vulnerable to adversarial attacks and distribution shifts, which can affect diagnostic reliability and undermine trust among healthcare professionals. In this study, we propose a robust training algorithm with data augmentation (RTDA) to mitigate these vulnerabilities in medical image classification. We benchmark classifier robustness against adversarial perturbations and natural variations of RTDA and six competing baseline techniques, including adversarial training and data augmentation approaches in isolation and combination, using experimental data sets with three different imaging technologies (mammograms, X-rays, and ultrasound). We demonstrate that RTDA achieves superior robustness against adversarial attacks and improved generalization performance in the presence of distribution shift in each image classification task while maintaining high clean accuracy.