Robust Training with Data Augmentation for Medical Imaging Classification

2025/06/20 by Martínez-Martínez, Josué, Brown, Olivia, Karami, Mostafa +1 · 1 citation
Computer Science · Medicine · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.17133

openalex publication_date 2025/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Tools and Practices for Responsible AI Engineering
• PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
• FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
• AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
• Theoretically Principled Trade-off between Robustness and Accuracy
• Adversarial Logit Pairing
• mixup: Beyond Empirical Risk Minimization
• Towards Deep Learning Models Resistant to Adversarial Attacks
• An overview of gradient descent optimization algorithms
• Deep Residual Learning for Image Recognition
• Explaining and Harnessing Adversarial Examples
• An overview of statistical learning theory
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning
• VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY

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