# Dynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification

arXiv: https://arxiv.org/abs/2403.06798 · HTML: https://mlsanity.com/paper/2403.06798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [On the (Statistical) Detection of Adversarial Examples](https://mlsanity.com/paper/1702.06280/) (2017)
- [Learnable Boundary Guided Adversarial Training](https://mlsanity.com/paper/2011.11164/) (2020)
- [Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training](https://mlsanity.com/paper/1711.06606/) (2017)
- [Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets](https://mlsanity.com/paper/1910.08051/) (2019)
- [Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification](https://mlsanity.com/paper/2102.01284/) (2021)
- [Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection](https://mlsanity.com/paper/2007.05225/) (2020)
- [Melanoma Detection using Adversarial Training and Deep Transfer Learning](https://mlsanity.com/paper/2004.06824/) (2020)
- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training](https://mlsanity.com/paper/2012.01166/) (2020)
- [Robust Kalman Filtering under Model Perturbations](https://mlsanity.com/paper/1508.01906/) (2015)
- [Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection](https://mlsanity.com/paper/2206.01736/) (2022)
- [Adaptive perturbation adversarial training: based on reinforcement learning](https://mlsanity.com/paper/2108.13239/) (2021)

## Abstract (arXiv)

Remarkable successes were made in Medical Image Classification (MIC) recently, mainly due to wide applications of convolutional neural networks (CNNs). However, adversarial examples (AEs) exhibited imperceptible similarity with raw data, raising serious concerns on network robustness. Although adversarial training (AT), in responding to malevolent AEs, was recognized as an effective approach to improve robustness, it was challenging to overcome generalization decline of networks caused by the AT. In this paper, in order to reserve high generalization while improving robustness, we proposed a dynamic perturbation-adaptive adversarial training (DPAAT) method, which placed AT in a dynamic learning environment to generate adaptive data-level perturbations and provided a dynamically updated criterion by loss information collections to handle the disadvantage of fixed perturbation sizes in conventional AT methods and the dependence on external transference. Comprehensive testing on dermatology HAM10000 dataset showed that the DPAAT not only achieved better robustness improvement and generalization preservation but also significantly enhanced mean average precision and interpretability on various CNNs, indicating its great potential as a generic adversarial training method on the MIC.