  In this work, we exploit the unsupervised domain adaptation problem for
radiology image interpretation across domains. Specifically, we study how to
adapt the disease recognition model from a labeled source domain to an
unlabeled target domain, so as to reduce the effort of labeling each new
dataset. To address the shortcoming of cross-domain, unpaired image-to-image
translation methods which typically ignore class-specific semantics, we propose
a task-driven, discriminatively trained, cycle-consistent generative
adversarial network, termed TUNA-Net. It is able to preserve 1) low-level
details, 2) high-level semantic information and 3) mid-level feature
representation during the image-to-image translation process, to favor the
target disease recognition task. The TUNA-Net framework is general and can be
readily adapted to other learning tasks. We evaluate the proposed framework on
two public chest X-ray datasets for pneumonia recognition. The TUNA-Net model
can adapt labeled adult chest X-rays in the source domain such that they appear
as if they were drawn from pediatric X-rays in the unlabeled target domain,
while preserving the disease semantics. Extensive experiments show the
superiority of the proposed method as compared to state-of-the-art unsupervised
domain adaptation approaches. Notably, TUNA-Net achieves an AUC of 96.3% for
pediatric pneumonia classification, which is very close to that of the
supervised approach (98.1%), but without the need for labels on the target
domain.
