Abstract: Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which requires a significant amount of expert annotated samples that are ...

We follow the MMSegmentation Dataset Preparation to download and setup the test sets. It is recommended to arrange the dataset as the following. If your folder structure is different, you may need to ...

Unsupervised domain adaptation (UDA) involves learning class semantics from labeled data within a source domain that generalize to an unseen target domain. UDA methods are particularly impactful for ...

Abstract: The recent emergence of the Segment Anything Model (SAM) enables various domain-specific segmentation tasks to be tackled cost-effectively by using bounding boxes as prompts. However, in ...