发表机构
Malaviya National Institute of Technology Jaipur; Côte d’Azur University; Khalifa University; Biomedical Perception & Intelligence Lab, University of South Dakota（斋浦尔马尔维亚国家技术学院; 蔚蓝海岸大学; 哈里发大学; 南达科他大学的生物医学感知与智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对结肠镜息肉分割难题，提出Lite-Polyp Inductor框架，通过基础模型归纳生成特定原型表示并语义对齐，引入Transformer融合突出相关表示，显著改进轻量级基线，以低计算开销实现卓越泛化性能。
AI 中文摘要
结肠镜检查中的自动息肉分割因息肉外观差异大及边界不清晰而面临挑战。尽管像DINOv2、SAM和OneFormer等基础模型有出色泛化能力，但因缺乏大规模标注数据和高计算需求，难以直接用于息肉分割任务及实时临床应用。此外，多个基础模型一起使用也有问题。轻量级模型虽计算高效，但泛化能力有限。为此提出Lite-Polyp Inductor（Lite-Pi）框架，通过基于重建的监督生成特定基础模型的原型表示并语义对齐，引入基于Transformer的融合突出息肉相关表示。在五个息肉分割基准数据集上的实验表明，Lite-Pi显著改进轻量级基线，以最小计算开销实现卓越泛化性能，为广义息肉分割提供实用解决方案。代码可在GitHub获取。
英文摘要
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
