In this paper we tackle the problem of unsupervised domain adaptation for the task of semantic segmentation, where we attempt to transfer the knowledge learned upon synthetic datasets with ground-truth labels to real-world images without any annotation. With the hypothesis that the structural content of images is the most informative and decisive factor to semantic segmentation and can be readily shared across domains, we propose a Domain Invariant Structure Extraction (DISE) framework to disentangle images into domain-invariant structure and domain-specific texture representations, which can further realize imagetranslation across domains and enable label transfer to improve segmentation performance. Extensive experiments verify the effectiveness of our proposed DISE model and demonstrate its superiority over several state-of-the-art approaches.

Findings from this study suggest that there exists an urgent need for accreditation in terms of pharmacovigilance of clinical sites and their practicing physicians for the prevention of irrational concomitant prescription in Taiwan. Our findings also indicate that it is necessary to investigate other possible conditions of potentially dangerous co-medication in Taiwan and other developing countries.

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A national education program facilitated by pharmacists can improve the medication knowledge of the participants. Pharmacists should be encouraged to play a proactive role in large-scale health education programs.

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