Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt

• URL: http://arxiv.org/abs/2510.15849v1
• Date: Fri, 17 Oct 2025 17:42:28 GMT
• Title: Memory-SAM: Human-Prompt-Free Tongue Segmentation via Retrieval-to-Prompt
• Abstract summary: Memory-SAM is a training-free, human-prompt-free pipeline.<n>It automatically generates effective prompts from a small memory of prior cases.<n>Results indicate that retrieval-to-prompt enables data-efficient, robust segmentation of irregular boundaries in tongue imaging.
• Score: 4.712714373340359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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