Abstract: Medical image segmentation is crucial for clinical decision-making, but the scarcity of annotated data presents significant challenges. Few-shot segmentation (FSS) methods show promise but ...

Abstract: Deep learning models for medical image segmentation often struggle with task-specific characteristics, limiting their generalization to unseen tasks with new anatomies, labels, or modalities ...

The performance gap between unsupervised segmentation models and SAM can be significantly reduced. UnSAM not only advances the state-of-the-art in unsupervised segmentation by 10% but also achieves ...