提升医学图像分割的稳定性，解决提示框误差与阈值不确定性问题
SAIF: A Stability-Aware Inference Framework for Medical Image Segmentation with Segment Anything Model
- 通过扰动提示框和阈值构建联合不确定空间
- 用稳定性和边界一致性筛选候选结果，提升分割可靠性
- 无需训练或修改模型，适合实际医疗场景部署
Segment Anything Model (SAM) 能实现可扩展的医学图像分割，但在作为冻结主干部署时存在推理阶段不稳定性。实践中，边界框提示常含定位误差，固定阈值二值化引入额外决策不确定性。这两者共同导致预测方差大，尤其在物体边界处，降低可靠性。我们提出稳定性感知推理框架（SAIF），一种无需训练、即插即用的推理方法，通过显式建模提示与阈值不确定性来增强鲁棒性。SAIF 通过结构化框扰动和阈值变化构建联合不确定性空间，利用决策稳定性与边界一致性评估每个假设，并引入稳定性-一致性评分过滤不稳定候选，实现概率空间中的加权融合。在 Synapse、CVC-ClinicDB、Kvasir-SEG 和 CVC-300 四个数据集上的实验表明，SAIF 持续提升分割准确率与鲁棒性，达到无需重训练或架构修改的最新水平。匿名代码已公开于 https://anonymous.4open.science/r/SAIF。
原文摘要 · Abstract (English)
Segment Anything Model (SAM) enable scalable medical image segmentation but suffer from inference-time instability when deployed as a frozen backbone. In practice, bounding-box prompts often contain localization errors, and fixed threshold binarization introduces additional decision uncertainty. These factors jointly cause high prediction variance, especially near object boundaries, degrading reliability. We propose the Stability-Aware Inference Framework (SAIF), a training-free and plug-and-play inference framework that improves robustness by explicitly modeling prompt and threshold uncertainty. SAIF constructs a joint uncertainty space via structured box perturbations and threshold variations, evaluates each hypothesis using decision stability and boundary consistency, and introduces a stability-consistency score to filter unstable candidates and perform stability-weighted fusion in probability space. Experiments on Synapse, CVC-ClinicDB, Kvasir-SEG, and CVC-300 demonstrate that SAIF consistently improves segmentation accuracy and robustness, achieving state-of-the-art performance without retraining or architectural modification. Our anonymous code is released at https://anonymous.4open.science/r/SAIF.
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。