Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
This addresses challenges in medical image diagnosis for clinical applications, but it appears incremental as it builds on existing UDA methods.
The paper tackles the problem of limited labeled samples and label noise in medical image diagnosis by proposing a Collaborative Unsupervised Domain Adaptation algorithm that uses rich labeled data from relevant domains, achieving promising empirical results.
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.