# Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation

arXiv: https://arxiv.org/abs/2105.12106 · HTML: https://mlsanity.com/paper/2105.12106/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [Exploring the Landscape of Spatial Robustness](https://mlsanity.com/paper/1712.02779/) (2017)
- [Domain-adversarial neural networks to address the appearance variability of histopathology images](https://mlsanity.com/paper/1707.06183/) (2017)
- [Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors](https://mlsanity.com/paper/2006.06356/) (2020)
- [Automatic segmentation of MR brain images with a convolutional neural network](https://mlsanity.com/paper/1704.03295/) (2017)
- [Adversarial Framing for Image and Video Classification](https://mlsanity.com/paper/1812.04599/) (2018)
- [Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs](https://mlsanity.com/paper/1701.08816/) (2017)
- [Learning More Robust Features with Adversarial Training](https://mlsanity.com/paper/1804.07757/) (2018)
- [Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks](https://mlsanity.com/paper/1708.00710/) (2017)

## Built on by (most cited first, 3 of 3)

- [Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges](https://mlsanity.com/paper/2303.14133/) (2023)
- [Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context](https://mlsanity.com/paper/2311.17524/) (2023)
- [Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration](https://mlsanity.com/paper/2509.21530/) (2025)

## Abstract (arXiv)

Segmentation is considered to be a very crucial task in medical image analysis. This task has been easier since deep learning models have taken over with its high performing behavior. However, deep learning models dependency on large data proves it to be an obstacle in medical image analysis because of insufficient data samples. Several data augmentation techniques have been used to mitigate this problem. We propose a new augmentation method by introducing adversarial learning attack techniques, specifically Fast Gradient Sign Method (FGSM). Furthermore, We have also introduced the concept of Inverse FGSM (InvFGSM), which works in the opposite manner of FGSM for the data augmentation. This two approaches worked together to improve the segmentation accuracy, as well as helped the model to gain robustness against adversarial attacks. The overall analysis of experiments indicates a novel use of adversarial machine learning along with robustness enhancement.