用少量标注数据实现精准肝部医学图像分割，提升效率与泛化能力。
Annotation-Efficient Task Guidance for Medical Segment Anything
- 利用辅助分类器的激活图引导半监督分割分支，实现多任务协同训练。
- 仅用0.04%标注数据训练90%更少轮次，Dice提升5.1%；跨域任务提升25.4%。
- 适合资源有限但需高精度分割的医疗影像研究者与临床应用开发团队。
医学图像分割是影像流程中的关键任务，影响众多基于图像的决策。传统全监督分割模型依赖大量人工标注数据，该过程成本高、耗时长且易出错。因此亟需高效、自动、标注省力的训练方法。本文提出SAM-Mix，一种面向医学图像分割的新型多任务学习框架，利用辅助分类器生成的类别激活图，指导基于SAM框架的半监督分割分支。在公开的LiTS数据集上的实验表明，该方法可同时完成肝脏的分类与分割。当仅使用50张标注的2D切片（占可用标注数据的0.04%），且训练轮次减少90%时，其Dice得分比最优基线模型提升5.1%。跨域分割任务中，相同配置下Dice提升达25.4%。代码已开源：https://github.com/tbwa233/SAM-Mix。
原文摘要 · Abstract (English)
Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation, which can be an expensive, time-consuming, and error-prone process. This signals a need for accurate, automatic, and annotation-efficient methods of training these models. We propose SAM-Mix, a novel multitask learning framework for medical image segmentation that uses class activation maps produced by an auxiliary classifier to guide the predictions of the semi-supervised segmentation branch, which is based on the SAM framework. Experimental evaluations on the public LiTS dataset confirm the effectiveness of SAM-Mix for simultaneous classification and segmentation of the liver from abdominal computed tomography (CT) scans. When trained for 90% fewer epochs on only 50 labeled 2D slices, representing just 0.04% of the available labeled training data, SAM-Mix achieves a Dice improvement of 5.1% over the best baseline model. The generalization results for SAM-Mix are even more impressive, with the same model configuration yielding a 25.4% Dice improvement on a cross-domain segmentation task. Our code is available at https://github.com/tbwa233/SAM-Mix.
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