测试SAM3在病理图像分割中的表现，发现其对文本提示不敏感，需领域适配。
Is SAM3 ready for pathology segmentation?
- 用文本和视觉提示评估SAM3在病理图像上的表现
- 纯文本提示难以激活细胞概念，性能依赖视觉提示类型
- 少样本学习有提升但对噪声敏感，需专门适配模型
SAM3能否实现任意病理图像的分割？数字病理分割涵盖组织与细胞层面，传统方法常因标注成本高且泛化能力差而受限。SAM3引入可提示概念分割，可通过文本提示实现自动化界面。本文提出系统性评估协议，结构化考察SAM3在不同监督设置下的能力：零样本、少样本及监督学习，采用多种提示策略。在NuInsSeg、PanNuke和GlaS等病理数据集上的广泛评估显示：（1）仅使用文本提示难以激活细胞概念；（2）性能高度依赖视觉提示类型与提示数量；（3）少样本学习带来收益，但对视觉提示噪声缺乏鲁棒性；（4）基于提示的使用与任务训练适配器之间仍存在显著差距。本研究厘清了SAM3在病理图像分割中的边界，并为领域适配的必要性提供实践指导。
原文摘要 · Abstract (English)
Is Segment Anything Model 3 (SAM3) capable in segmenting Any Pathology Images? Digital pathology segmentation spans tissue-level and nuclei-level scales, where traditional methods often suffer from high annotation costs and poor generalization. SAM3 introduces Promptable Concept Segmentation, offering a potential automated interface via text prompts. With this work, we propose a systematic evaluation protocol to explore the capability space of SAM3 in a structured manner. Specifically, we evaluate SAM3 under different supervision settings including zero-shot, few-shot, and supervised with varying prompting strategies. Our extensive evaluation on pathological datasets including NuInsSeg, PanNuke and GlaS, reveals that: (1) text-only prompts poorly activate nuclear concepts; (2) performance is highly sensitive to visual prompt types and budgets; (3) few-shot learning offers gains, but SAM3 lacks robustness against visual prompt noise; and (4) a significant gap persists between prompt-based usage and task-trained adapter-based reference. Our study delineates SAM3's boundaries in pathology image segmentation and provides practical guidance on the necessity of pathology domain adaptation.
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