无需人工提示，用记忆检索自动生成分割指令，精准分割舌头图像。
Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt
- 从少量历史案例中检索并生成点提示，驱动SAM2分割。
- 在600张图像上达到mIoU 0.9863，显著优于基线方法。
- 适合中医舌诊、医疗影像等需低标注成本的场景。
准确的舌头分割对可靠的中医分析至关重要。监督模型依赖大量标注数据，而SAM系列模型仍需人工提示。我们提出Memory-SAM，一种无需训练、无需人工提示的管道，通过稀疏的DINOv3特征与FAISS检索，从少量历史案例中自动生成有效提示。给定查询图像，通过掩码约束匹配检索到的样例，提炼出前景/背景点提示，引导SAM2完成分割，无需手动点击或模型微调。我们在600张专家标注图像（300张受控，300张真实场景）上评估。在混合测试集上，Memory-SAM实现mIoU 0.9863，超越FCN（0.8188）和检测器转框的SAM基线（0.1839）。在受控数据上，因标注变异性导致天花板效应高于0.98，细微差异意义不大；但在真实条件下，本方法仍显示明显优势。结果表明，检索-提示机制可实现数据高效、鲁棒的不规则边界分割。代码已公开于https://github.com/jw-chae/memory-sam。
原文摘要 · Abstract (English)
Accurate tongue segmentation is crucial for reliable TCM analysis. Supervised models require large annotated datasets, while SAM-family models remain prompt-driven. We present Memory-SAM, a training-free, human-prompt-free pipeline that automatically generates effective prompts from a small memory of prior cases via dense DINOv3 features and FAISS retrieval. Given a query image, mask-constrained correspondences to the retrieved exemplar are distilled into foreground/background point prompts that guide SAM2 without manual clicks or model fine-tuning. We evaluate on 600 expert-annotated images (300 controlled, 300 in-the-wild). On the mixed test split, Memory-SAM achieves mIoU 0.9863, surpassing FCN (0.8188) and a detector-to-box SAM baseline (0.1839). On controlled data, ceiling effects above 0.98 make small differences less meaningful given annotation variability, while our method shows clear gains under real-world conditions. Results indicate that retrieval-to-prompt enables data-efficient, robust segmentation of irregular boundaries in tongue imaging. The code is publicly available at https://github.com/jw-chae/memory-sam.
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