标题： 分层自提示SAM：一种无需提示的医学图像分割框架 显示英文标题

标题： Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework

摘要： 尽管 Segment Anything Model (SAM) 在自然图像分割方面非常有效，但它依赖于提示，这限制了它在医疗成像中的适用性，在医疗成像中手动提示通常是不可用的。 现有针对医学分割微调 SAM 的努力通常难以去除这种依赖性。 我们提出了分层自提示 SAM（HSP-SAM），这是一种新颖的自提示框架，使 SAM 能够在无提示的医学图像分割中实现强大的性能。 与以往局限于类似 vanilla SAM 的位置提示的自提示方法不同，我们首次在自提示过程中引入了抽象提示的学习。 这个简单直观的自提示框架在诸如结肠息肉和皮肤病变分割的经典分割任务中表现出色，同时在多种医学成像模态上保持了鲁棒性。 此外，它在未见过的数据集上表现出强大的泛化能力，在某些具有挑战性的基准测试中比以前最先进的方法提高了多达 14.04%。 这些结果表明，抽象提示相较于位置提示封装了更丰富、更高维度的语义信息，从而增强了模型的鲁棒性和泛化性能。 所有模型和代码将在接受后发布。 显示英文摘要

摘要： Although the Segment Anything Model (SAM) is highly effective in natural image segmentation, it requires dependencies on prompts, which limits its applicability to medical imaging where manual prompts are often unavailable. Existing efforts to fine-tune SAM for medical segmentation typically struggle to remove this dependency. We propose Hierarchical Self-Prompting SAM (HSP-SAM), a novel self-prompting framework that enables SAM to achieve strong performance in prompt-free medical image segmentation. Unlike previous self-prompting methods that remain limited to positional prompts similar to vanilla SAM, we are the first to introduce learning abstract prompts during the self-prompting process. This simple and intuitive self-prompting framework achieves superior performance on classic segmentation tasks such as polyp and skin lesion segmentation, while maintaining robustness across diverse medical imaging modalities. Furthermore, it exhibits strong generalization to unseen datasets, achieving improvements of up to 14.04% over previous state-of-the-art methods on some challenging benchmarks. These results suggest that abstract prompts encapsulate richer and higher-dimensional semantic information compared to positional prompts, thereby enhancing the model's robustness and generalization performance. All models and codes will be released upon acceptance.