Abstract
Universal models for medical image segmentation, such as interactive and in-context learning (ICL) models, offer strong generalization but require extensive annotations. Interactive models need repeated user prompts for each image, while ICL relies on dense, pixel-level labels. To address this, we propose Weakly Supervised In-Context Learning (WS-ICL), a new ICL paradigm that leverages weak prompts (e.g., bounding boxes or points) instead of dense labels for context. This approach significantly reduces annotation effort by eliminating the need for fine-grained masks and repeated user prompting for all images. We evaluated the proposed WS-ICL model on three held-out benchmarks. Experimental results demonstrate that WS-ICL achieves performance comparable to regular ICL models at a significantly lower annotation cost. In addition, WS-ICL is highly competitive even under the interactive paradigm. These findings establish WS-ICL as a promising step toward more efficient and unified universal models for medical image segmentation. Our code and model are publicly available at this https URL.
Abstract (translated)
通用医学图像分割模型，如交互式和上下文内学习（ICL）模型，虽然具有很强的泛化能力，但需要大量的标注数据。交互式模型要求用户对每个图像进行多次提示输入，而ICL则依赖于密集、像素级别的标签。为解决这一问题，我们提出了弱监督上下文内学习（WS-ICL），这是一种新的ICL范式，它利用弱提示（例如边界框或点）而不是密集标签来提供上下文信息。这种方法通过消除对精细标注掩模的需求以及所有图像的重复用户提示需求，显著减少了注释工作量。 我们在三个独立的基准测试集上评估了所提出的WS-ICL模型。实验结果表明，在大大减少标注成本的情况下，WS-ICL与常规ICL模型相比能够达到相似的效果表现。此外，即使在交互式范式下，WS-ICL也表现出很强的竞争性。 这些发现证明了WS-ICL作为更高效和统一的医学图像分割通用模型的重要一步。我们的代码和模型可以在以下链接公开获取：[此处插入URL]。
URL
https://arxiv.org/abs/2510.05899
