  In this paper, for the first time, we propose an evaluation method for deep
learning models that assesses the performance of a model not only in an unseen
test scenario, but also in extreme cases of noise, outliers and ambiguous input
data. To this end, we utilize adversarial examples, images that fool machine
learning models, while looking imperceptibly different from original data, as a
measure to evaluate the robustness of a variety of medical imaging models.
Through extensive experiments on skin lesion classification and whole brain
segmentation with state-of-the-art networks such as Inception and UNet, we show
that models that achieve comparable performance regarding generalizability may
have significant variations in their perception of the underlying data
manifold, leading to an extensive performance gap in their robustness.
