CoRE：基于概念推理的连续脑病变分割
CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentation
AI总结：
本文提出CoRE框架，通过整合视觉特征与结构化概念，解决连续学习中容量限制和冗余参数增长问题，实现基于临床先验的模型进化。
摘要：
准确的脑部病变MRI分割对临床诊断和治疗计划至关重要。由于标注成本高和数据隐私严格，通用模型需采用持续学习（CL）适应演进的临床任务而不丢失先前知识。然而，现有CL范式常面临容量限制或冗余参数增长，甚至先进动态方法大多依赖图像感知策略，难以处理脑成像中显著的病理和多模态异质性。为此，我们提出概念推理扩展（CoRE）框架，通过整合视觉特征与结构化概念建立联合决策机制。通过将图像标记与层次化概念库对齐，CoRE模拟临床推理，指导可解释的专家路由和基于需求的模型增长。这一协作过程确保模型进化基于临床先验，防止冗余参数增长同时最大化知识重用。在12个连续脑部病变MRI任务上的广泛评估表明，CoRE实现了最先进的性能，并为高效未来适应提供了高知识起点。其优越的少样本迁移能力和临床可解释性进一步验证了其在非平稳临床数据流中的有效性。我们的代码将很快发布。
英文摘要：
Accurate brain lesion segmentation in MRI is vital for effective clinical diagnosis and treatment planning. Due to high annotation costs and strict data privacy regulations, universal models require employing Continual Learning (CL) to adapt to evolving clinical tasks without losing previously acquired knowledge. However, existing CL paradigms often suffer from capacity limits or redundant parameter growth, and even advanced dynamic methods rely mostly on image-perception strategies that struggle to handle the substantial pathological and multimodal heterogeneity inherent in brain imaging. To address this issue, we propose Concept-Reasoning Expansion (CoRE) framework, which establishes a joint decision-making mechanism by integrating visual features with structured concepts. Through the alignment of image tokens with a hierarchical concept library, CoRE simulates clinical reasoning to guide both interpretable expert routing and demand-based model growth. This collaborative process ensures model evolution is grounded in clinical priors, preventing redundant parameter expansion while maximizing knowledge reuse. Extensive evaluations across 12 sequential brain lesion MRI tasks demonstrate that CoRE achieves state-of-the-art performance and provides a high knowledge starting point for efficient future adaptation. Its superior few-shot transferability and clinical interpretability further validate its effectiveness in managing non-stationary clinical data streams. Our code will be released soon.
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。
