  Deep learning based medical image diagnosis has shown great potential in
clinical medicine. However, it often suffers two major difficulties in
practice: 1) only limited labeled samples are available due to expensive
annotation costs over medical images; 2) labeled images may contain
considerable label noises (e.g., mislabeling labels) due to diagnostic
difficulties. In this paper, we seek to exploit rich labeled data from relevant
domains to help the learning in the target task with unsupervised domain
adaptation (UDA). Unlike most existing UDA methods which rely on clean labeled
data or assume samples are equally transferable, we propose a novel
Collaborative Unsupervised Domain Adaptation algorithm to conduct
transferability-aware domain adaptation and conquer label noise in a
cooperative way. Promising empirical results verify the superiority of the
proposed method.
