  Image segmentation is important in medical imaging, providing valuable,
quantitative information for clinical decision-making in diagnosis, therapy,
and intervention. The state-of-the-art in automated segmentation remains
supervised learning, employing discriminative models such as U-Net. However,
training these models requires access to large amounts of manually labelled
data which is often difficult to obtain in real medical applications. In such
settings, semi-supervised learning (SSL) attempts to leverage the abundance of
unlabelled data to obtain more robust and reliable models. Recently, generative
models have been proposed for semantic segmentation, as they make an attractive
choice for SSL. Their ability to capture the joint distribution over input
images and output label maps provides a natural way to incorporate information
from unlabelled images. This paper analyses whether deep generative models such
as the SemanticGAN are truly viable alternatives to tackle challenging medical
image segmentation problems. To that end, we thoroughly evaluate the
segmentation performance, robustness, and potential subgroup disparities of
discriminative and generative segmentation methods when applied to large-scale,
publicly available chest X-ray datasets.
