Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation
This work addresses data scarcity in medical image analysis, offering an incremental improvement over existing augmentation techniques.
The authors tackled the problem of insufficient data for medical image segmentation by proposing a new data augmentation method using adversarial learning techniques, specifically FGSM and InvFGSM, which improved segmentation accuracy and enhanced model robustness against adversarial attacks.
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.