OFL-SAM2: Efficient Medical Image Segmentation with Prompt-Free SAM2 and Online Few-shot Learning
Research Paper#Medical Image Segmentation, Few-shot Learning, SAM2🔬 Research|Analyzed: Jan 3, 2026 06:23•
Published: Dec 31, 2025 13:41
•1 min read
•ArXivAnalysis
This paper addresses the challenge of adapting the Segment Anything Model 2 (SAM2) for medical image segmentation (MIS), which typically requires extensive annotated data and expert-provided prompts. OFL-SAM2 offers a novel prompt-free approach using a lightweight mapping network trained with limited data and an online few-shot learner. This is significant because it reduces the reliance on large, labeled datasets and expert intervention, making MIS more accessible and efficient. The online learning aspect further enhances the model's adaptability to different test sequences.
Key Takeaways
- •Proposes OFL-SAM2, a prompt-free SAM2 framework for medical image segmentation.
- •Utilizes a lightweight mapping network and online few-shot learning to reduce reliance on extensive labeled data.
- •Achieves state-of-the-art performance on diverse MIS datasets with limited training data.
- •Introduces an adaptive fusion module to integrate target features with SAM2's memory-attention features.
Reference / Citation
View Original"OFL-SAM2 achieves state-of-the-art performance with limited training data."