用动态专家混合与知识提示，实现跨肿瘤类型精准分割。
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts
- 引入动态专家混合与知识驱动提示，融合医学先验知识。
- 平均DSC提升5.20%，可训练参数减少91.04%。
- 适合临床部署，兼顾精度与计算效率。
精准肿瘤分割对癌症诊断与治疗至关重要。尽管基础模型推动了通用分割发展，现有方法仍面临三大挑战：（1）医学先验知识融入不足，（2）通用特征与肿瘤特异性特征失衡，（3）临床适配计算成本高。为此，我们提出MAST-Pro（面向泛肿瘤自适应分割的专家混合框架），结合动态专家混合（D-MoE）与知识驱动提示，实现跨器官肿瘤分割。文本与解剖提示提供领域先验，引导肿瘤表征学习；D-MoE动态选择专家，平衡通用与肿瘤特异性特征学习，提升多类型肿瘤分割精度。为提高效率，采用参数高效微调（PEFT），显著降低计算开销。在多个解剖部位肿瘤数据集上的实验表明，MAST-Pro优于当前最优方法，平均Dice相似系数（DSC）最高提升5.20%，可训练参数减少91.04%，且不损失精度。
原文摘要 · Abstract (English)
Accurate tumor segmentation is crucial for cancer diagnosis and treatment. While foundation models have advanced general-purpose segmentation, existing methods still struggle with: (1) limited incorporation of medical priors, (2) imbalance between generic and tumor-specific features, and (3) high computational costs for clinical adaptation. To address these challenges, we propose MAST-Pro (Mixture-of-experts for Adaptive Segmentation of pan-Tumors with knowledge-driven Prompts), a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation. Specifically, text and anatomical prompts provide domain-specific priors, guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning, improving segmentation accuracy across diverse tumor types. To enhance efficiency, we employ Parameter-Efficient Fine-Tuning (PEFT), optimizing MAST-Pro with significantly reduced computational overhead. Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average DSC while reducing trainable parameters by 91.04%, without compromising accuracy.
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