用大模型知识增强轻量分割模型，提升肠息肉识别泛化能力。
Induce to Empower: Improving Lightweight Baselines via Foundation Model Induction for Generalized Polyp Segmentation
- 通过重建监督生成大模型专属原型，对齐语义先验
- 融合多大模型互补信息，显著提升边界与形态识别准确率
- 轻量部署无额外计算负担，适合临床实时应用
结肠镜下自动肠息肉分割因外观差异大、边界模糊仍具挑战。尽管DINOv2、SAM、OneFormer等基础模型（FMs）表现出强泛化能力，但其直接迁移至息肉分割任务并部署于临床实时场景受限于缺乏大规模标注数据及高计算开销。同时，多模型联合使用虽可编码互补语义与结构信息，却带来额外复杂性。轻量级模型如U-Net、PraNet、U-Net++虽计算高效，却因表征能力有限，在跨数据集泛化上表现不佳。为此，我们提出Lite-Polyp Inductor（Lite-Pi），一种新型基础模型诱导框架，显著提升轻量级息肉分割基线性能。该框架生成针对各基础模型的原型表示，并通过基于重建的监督实现与对应模型先验的语义对齐。随后引入基于Transformer的融合机制，突出息肉相关特征（包括显著边界信息），同时保留互补语义线索。在五个息肉分割基准数据集上的广泛实验表明，Lite-Pi能显著提升轻量基线模型的泛化性能，以极小计算开销实现优越效果，为泛化息肉分割提供实用解决方案。代码已开源：https://github.com/lostinrepo/Lite-Pi。
原文摘要 · Abstract (English)
Automated polyp segmentation in colonoscopy continues to pose challenges due to substantial appearance variations and indistinct polyp boundaries. Although emerging foundation models (FMs) such as DINOv2, SAM, and OneFormer, demonstrate remarkable generalization capabilities, their direct transfer to the polyp segmentation task and deployment in real-time clinical settings are difficult due to lack of large-scale labeled data and high computational demands. In addition, adopting multiple FMs together raises concerns, even though they encode complementary semantic and structural information. While lightweight models, including U-Net, PraNet and U-Net++, are computationally efficient, they often struggle to generalize across datasets due to limited representational capacity. To address this gap, we propose Lite-Polyp Inductor (Lite-Pi), a novel foundation model induction framework that significantly enhances lightweight polyp segmentation baselines. Our proposed framework generates FM-specific prototype representations and aligns them semantically with the corresponding foundation model priors through reconstruction-based supervision. Subsequently, transformer-based fusion is introduced to highlight the polyp relevant representations, including salient boundary information, while preserving complementary semantic cues. Extensive experiments across five polyp segmentation benchmark datasets demonstrate that Lite-π significantly improves lightweight baselines, achieving superior generalization performance with minimal computational overhead and thereby, offering a practical solution for generalized polyp segmentation. Our code is available at GitHub. https://github.com/lostinrepo/Lite-Pi
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