Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation

Understand

Unsupervised domain adaptation (UDA) methods have shown their promising performance in the cross-modality medical image segmentation tasks.

• These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images.
• However, when there exists a large domain shift between source and target domains, we argue that this asymmetric structure could not fully eliminate the domain gap.
• In this paper, we present a novel deep symmetric architecture of UDA for medical image segmentation, which consists of a segmentation sub-network, and two symmetric source and target domain translation sub-networks.