  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.
