MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts

Runqi Meng; Sifan Song; Pengfei Jin; Yujin Oh; Lin Teng; Yulin Wang; Yiqun Sun; Ling Chen; Xiang Li; Quanzheng Li; Ning Guo; Dinggang Shen

arXiv · 2503.14355

MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts

Runqi Meng·Sifan Song·Pengfei Jin·Yujin Oh·Lin Teng·Yulin Wang·Yiqun Sun·Ling Chen·Xiang Li·Quanzheng Li·Ning Guo·Dinggang Shen

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Abstract

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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Runqi Meng, Sifan Song, Pengfei Jin, Yujin Oh, Lin Teng, Yulin Wang, Yiqun Sun, Ling Chen, Xiang Li, Quanzheng Li, Ning Guo, Dinggang Shen. 2025-03-18. MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts. https://arxiv.org/abs/2503.14355

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