  As segmentation labels are scarce, extensive researches have been conducted
to train segmentation networks with domain adaptation, semi-supervised or
self-supervised learning techniques to utilize abundant unlabeled dataset.
However, these approaches appear different from each other, so it is not clear
how these approaches can be combined for better performance. Inspired by recent
multi-domain image translation approaches, here we propose a novel segmentation
framework using adaptive instance normalization (AdaIN), so that a single
generator is trained to perform both domain adaptation and semi-supervised
segmentation tasks via knowledge distillation by simply changing task-specific
AdaIN codes. Specifically, our framework is designed to deal with difficult
situations in chest X-ray radiograph (CXR) segmentation, where labels are only
available for normal data, but the trained model should be applied to both
normal and abnormal data. The proposed network demonstrates great
generalizability under domain shift and achieves the state-of-the-art
performance for abnormal CXR segmentation.
