用少量样本生成自动提示，让SAM高效分割医学影像。
PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation
- 用原型捕捉类别特征，自动生成分割提示。
- 仅用10%切片数据，平均Dice达现有方法最优。
- 适合医疗图像少样本分割，无需手动标注提示。
Segment Anything Model (SAM) 展现了强大的通用分割能力，但将其应用于医学图像分割需大量像素级标注和精确的点/框提示设计。为此，我们提出 PGP-SAM，一种基于原型的少样本微调方法，仅用少量样本即可替代繁琐的人工提示。核心思想是利用类间与类内原型捕获特定类别知识及关系。提出两个关键组件：(1) 即插即用的上下文调制模块，整合多尺度信息；(2) 类别引导的交叉注意力机制，融合原型与特征以实现自动提示生成。在公开多器官数据集和私有心室数据集上的实验表明，PGP-SAM 在仅使用 10% 2D 切片的情况下，显著优于现有无提示的 SAM 变体，达到更优的平均 Dice 分数。
原文摘要 · Abstract (English)
The Segment Anything Model (SAM) has demonstrated strong and versatile segmentation capabilities, along with intuitive prompt-based interactions. However, customizing SAM for medical image segmentation requires massive amounts of pixel-level annotations and precise point- or box-based prompt designs. To address these challenges, we introduce PGP-SAM, a novel prototype-based few-shot tuning approach that uses limited samples to replace tedious manual prompts. Our key idea is to leverage inter- and intra-class prototypes to capture class-specific knowledge and relationships. We propose two main components: (1) a plug-and-play contextual modulation module that integrates multi-scale information, and (2) a class-guided cross-attention mechanism that fuses prototypes and features for automatic prompt generation. Experiments on a public multi-organ dataset and a private ventricle dataset demonstrate that PGP-SAM achieves superior mean Dice scores compared with existing prompt-free SAM variants, while using only 10\% of the 2D slices.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。