Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging

Unsupervised Domain Adaptation for Joint Segmentation and POS-Tagging

Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation

Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain. Although UDA is …

Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation

To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learnin…

Unsupervised domain adaptation based COVID-19 CT infection segmentation network

Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnosis approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accu…

Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision

Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe annotation of these masks can be very expen…