Is SAM3 ready for pathology segmentation?

• URL: http://arxiv.org/abs/2604.18225v1
• Date: Mon, 20 Apr 2026 13:10:07 GMT
• Title: Is SAM3 ready for pathology segmentation?
• Abstract summary: SAM3 introduces Promptable Concept, offering a potential automated interface via text prompts.<n>We evaluate SAM3 under different supervision settings including zero-shot, few-shot, and supervised with varying prompting strategies.<n>Our study delineates SAM3's boundaries in pathology image segmentation and provides practical guidance on the necessity of pathology domain adaptation.
• Score: 13.272947729025027
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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