通过不确定性调控，让大模型同时兼顾通用与专有分割能力。
Harmonizing Generalization and Specialization: Uncertainty-Informed Collaborative Learning for Semi-supervised Medical Image Segmentation
- 用双教师框架融合通用知识与任务特异性表示。
- 基于预测不确定性的自适应伪标签提升学习稳定性。
- 在少标注下接近全监督效果，适合临床罕见病分割。
视觉基础模型通过大规模异构预训练在医学图像分割中展现出强大泛化能力，但在标注有限或病理罕见的情况下，常因通用先验与任务需求不匹配而表现不佳。为此，我们提出不确定性引导的协同学习（UnCoL），一种双教师框架，实现半监督医学图像分割中的泛化与专化的平衡。UnCoL从冻结的基础模型中蒸馏视觉与语义表征以传递通用知识，同时保留一个逐步适应的教师模型以捕捉细粒度、任务特定特征。通过预测不确定性自适应调节伪标签学习，选择性抑制不可靠监督，稳定模糊区域的学习。在多种2D与3D分割基准测试中，UnCoL持续优于当前最先进方法及基础模型基线。此外，该模型在显著减少标注需求的前提下，达到接近全监督性能。
原文摘要 · Abstract (English)
Vision foundation models have demonstrated strong generalization in medical image segmentation by leveraging large-scale, heterogeneous pretraining. However, they often struggle to generalize to specialized clinical tasks under limited annotations or rare pathological variations, due to a mismatch between general priors and task-specific requirements. To address this, we propose Uncertainty-informed Collaborative Learning (UnCoL), a dual-teacher framework that harmonizes generalization and specialization in semi-supervised medical image segmentation. Specifically, UnCoL distills both visual and semantic representations from a frozen foundation model to transfer general knowledge, while concurrently maintaining a progressively adapting teacher to capture fine-grained and task-specific representations. To balance guidance from both teachers, pseudo-label learning in UnCoL is adaptively regulated by predictive uncertainty, which selectively suppresses unreliable supervision and stabilizes learning in ambiguous regions. Experiments on diverse 2D and 3D segmentation benchmarks show that UnCoL consistently outperforms state-of-the-art semi-supervised methods and foundation model baselines. Moreover, our model delivers near fully supervised performance with markedly reduced annotation requirements.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。