  Few-shot Learning (FSL) methods are being adopted in settings where data is
not abundantly available. This is especially seen in medical domains where the
annotations are expensive to obtain. Deep Neural Networks have been shown to be
vulnerable to adversarial attacks. This is even more severe in the case of FSL
due to the lack of a large number of training examples. In this paper, we
provide a framework to make few-shot segmentation models adversarially robust
in the medical domain where such attacks can severely impact the decisions made
by clinicians who use them. We propose a novel robust few-shot segmentation
framework, Prototypical Neural Ordinary Differential Equation (PNODE), that
provides defense against gradient-based adversarial attacks. We show that our
framework is more robust compared to traditional adversarial defense mechanisms
such as adversarial training. Adversarial training involves increased training
time and shows robustness to limited types of attacks depending on the type of
adversarial examples seen during training. Our proposed framework generalises
well to common adversarial attacks like FGSM, PGD and SMIA while having the
model parameters comparable to the existing few-shot segmentation models. We
show the effectiveness of our proposed approach on three publicly available
multi-organ segmentation datasets in both in-domain and cross-domain settings
by attacking the support and query sets without the need for ad-hoc adversarial
training.
