MSDAN: Multi-Scale Self-Attention Unsupervised Domain Adaptation Network for Thyroid Ultrasound Images

With the maturity of artificial intelligence, AI-aided diagnosis technology is gradually widely applied in clinical medicine. However, for the same pathological tissue, medical images produced by different types of instruments usually possess different data distributions. Because of the domain shift phenomenon, AI-aided diagnosis cannot accurately diagnose medical images in other domains, which is a waste of precious medical images. This paper proposes a Multi-Scale Self-Attention Unsupervised Domain Adaptive framework (MSDAN), which consists of three modules. First, the multi-scale framework constrains the source domain features and target domain features by optimizing adversarial losses with different level features. Second, the mix-up discriminator extracts latent spatial features by mixing up source domain and target domain features. Finally, MSDAN learns the geometric information of the pathological tissues in medical images through the self-attention module, thereby improving the transfer effect of the semantic information in medical images. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity, especially for thyroid ultrasound images.