  Medical image computing has advanced rapidly with the advent of deep learning
techniques such as convolutional neural networks. Deep convolutional neural
networks can perform exceedingly well given full supervision. However, the
success of such fully-supervised models for various image analysis tasks (e.g.,
anatomy or lesion segmentation from medical images) is limited to the
availability of massive amounts of labeled data. Given small sample sizes, such
models are prohibitively data biased with large domain shift. To tackle this
problem, we propose a novel end-to-end medical image segmentation model, namely
Progressive Adversarial Semantic Segmentation (PASS), which can make improved
segmentation predictions without requiring any domain-specific data during
training time. Our extensive experimentation with 8 public diabetic retinopathy
and chest X-ray datasets, confirms the effectiveness of PASS for accurate
vascular and pulmonary segmentation, both for in-domain and cross-domain
evaluations.
