SemiSAM：通过SAM辅助一致性正则化增强半监督医学图像分割
SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization
AI总结：
本文提出SemiSAM策略，利用SAM生成的伪标签作为额外监督，在标注极少时显著提升半监督医学图像分割性能，并具备即插即用效率。
摘要：
与全监督方法相比，半监督学习因其对获取专家大量标注的依赖较小而备受关注，这对医学图像分割尤为重要，因为该任务通常需要领域专家进行密集的像素/体素级标注。尽管半监督方法可以利用未标注数据提升性能，但在标注极其有限的场景下，与全监督方法之间仍存在差距。本文提出一种简单而高效的策略，探索利用Segment Anything Model（SAM）来增强半监督医学图像分割。具体而言，利用领域知识训练的分割模型提供定位信息并为SAM生成输入提示。随后，将SAM生成的伪标签用作额外监督，以辅助半监督框架的学习过程。大量实验表明，在仅有一个或少数几个标注图像可用时，SemiSAM能显著提升现有半监督框架的性能，并作为一种即插即用策略展现出很强的效率，适用于半监督医学图像分割。
英文摘要：
Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts. Although semi-supervised methods can improve the performance by utilizing unlabeled data, there are still gaps between fully supervised methods under extremely limited annotation scenarios. In this paper, we propose a simple yet efficient strategy to explore the usage of the Segment Anything Model (SAM) for enhancing semi-supervised medical image segmentation. Concretely, the segmentation model trained with domain knowledge provides information for localization and generating input prompts to the SAM. Then the generated pseudo-labels of SAM are utilized as additional supervision to assist in the learning procedure of the semi-supervised framework. Extensive experiments demonstrate that SemiSAM significantly improves the performance of existing semi-supervised frameworks when only one or a few labeled images are available and shows strong efficiency as a plug-and-play strategy for semi-supervised medical image segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
