发表机构
Rice University（莱斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对上下文医学图像分割，提出基于相似度的支持集选择方法，训练Transformer分类器预测分割失败，可提升性能并为临床安全部署提供机制。
AI 中文摘要
上下文学习（ICL）通过以特定任务的图像-掩码示例支持集为条件，无需重新训练即可使医学图像分割模型适配未见的结构与模态。由于该支持集是模型唯一的特定任务信号，其构成直接影响分割性能。本研究将支持集作为上下文学习可靠性的可控决定因素展开研究：首先，对比随机采样与基于相似度的选择方法，其中示例根据与查询图像的视觉相似度进行检索；其次，训练一个基于Transformer的分类器，仅从查询图像和支持图像中预测分割结果是否会低于指定的交并比（IoU）阈值。通过在四个基准数据集、三种成像模态上使用带有DINOv3嵌入的MultiverSeg模型，我们发现基于相似度的选择方法始终与随机采样相当或更优，且在最小支持集规模下增益最大；此外，该分类器在所有四个基准数据集上预测分割失败的表现均优于随机水平。最终，这些结果表明，可通过合理的支持集选择提升上下文分割的可靠性，并在使用前进行预测，为更安全的临床部署提供实用机制。
英文摘要
In-context learning (ICL) adapts medical image segmentation models to unseen structures and modalities without retraining by conditioning on a task-specific support set of image-mask exemplars. Because this support set is the model's only task-specific signal, its composition directly influences segmentation performance. In this work, we investigate the support set as a controllable determinant of ICL reliability. First, we compare random sampling against similarity-based selection, where exemplars are retrieved based on their visual similarity to the query image. Second, we train a transformer-based classifier to predict, from the query and support images alone, whether a segmentation will fall below a specified Intersection-over-Union (IoU) threshold. Using MultiverSeg with DINOv3 embeddings across four benchmarks and three imaging modalities, we show that similarity-based selection consistently matches or outperforms random sampling, with the largest gains at the smallest support set sizes. Furthermore, our classifier predicts segmentation failure above chance on all four benchmarks. Ultimately, these results demonstrate that the reliability of in-context segmentation can be both improved via informed support selection and anticipated before use, providing practical mechanisms for safer clinical deployment.
Comments10 pages
