  Thanks to their ability to learn data distributions without requiring paired
data, Generative Adversarial Networks (GANs) have become an integral part of
many computer vision methods, including those developed for medical image
segmentation. These methods jointly train a segmentor and an adversarial mask
discriminator, which provides a data-driven shape prior. At inference, the
discriminator is discarded, and only the segmentor is used to predict label
maps on test images. But should we discard the discriminator? Here, we argue
that the life cycle of adversarial discriminators should not end after
training. On the contrary, training stable GANs produces powerful shape priors
that we can use to correct segmentor mistakes at inference. To achieve this, we
develop stable mask discriminators that do not overfit or catastrophically
forget. At test time, we fine-tune the segmentor on each individual test
instance until it satisfies the learned shape prior. Our method is simple to
implement and increases model performance. Moreover, it opens new directions
for re-using mask discriminators at inference. We release the code used for the
experiments at https://vios-s.github.io/adversarial-test-time-training.
