Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models

Understand

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 often require training on the target domain and struggle to generalize across different modalities.
• Similarly, adapting foundation models like the Segment Anything Model (SAM) for medical imaging has limitations, including the need for finetuning and domain-specific adaptation.
• To address these issues, we propose a novel method that adapts DINOv2 and Segment Anything Model 2 (SAM 2) for retrieval-augmented few-shot medical image segmentation.