通过超轻量架构在商用CPU上实现实时结肠镜息肉分割
Enabling Real-Time Colonoscopic Polyp Segmentation on Commodity CPUs via Ultra-Lightweight Architecture
AI总结：
本文提出UltraSeg家族，一种在CPU上运行的轻量级分割模型，能够在不依赖GPU的情况下实现实时结肠镜息肉分割，其核心方法是采用组多率扩张卷积和注意力门控跨层融合，主要贡献是建立了首个在商用CPU上实现高精度实时息肉分割的基准线。
摘要：
实时息肉分割对于早期结直肠癌检测至关重要，但临床部署仍受GPU依赖的阻碍。我们引入UltraSeg家族，一组在CPU上运行的分割模型，参数量低于0.3M。UltraSeg-108K（0.108M）建立了极端压缩的前沿，而UltraSeg-130K（0.130M）通过跨层轻量融合提升了多中心泛化能力。该架构用组多率扩张卷积和注意力门控跨层融合取代参数密集的组件，实现了在单个CPU核心上实时吞吐（在256*256分辨率上超过50 FPS，在352*352分辨率上超过30 FPS）而不牺牲临床级精度。在七个公开数据集上评估，UltraSeg-130K在两个分辨率上均达到Dice分数超过0.8，显著优于所有现有的子0.3M竞争者。值得注意的是，在零样本外部验证中，它接近或超过了UNet-Medium（7.76M参数）的性能，但仅使用其1.7%的参数，建立了首个在CPU上实现实时息肉分割的强基准线。当扩展到4.38M参数时，UltraSeg的准确性可与重型最先进的模型相媲美，同时保持数量级的参数优势，证明了所提出的设计原则在效率光谱的整个范围内实现了内在的表示增益。通过提供首个在商用CPU上可部署的实时解决方案，本工作为资源有限的环境提供了一个立即可用的工具，并为超越内窥镜的实时医疗AI提供了可复现的蓝图。源代码已公开。
英文摘要：
Real-time polyp segmentation is essential for early colorectal cancer detection, yet clinical deployment remains blocked by GPU dependency. We introduce the UltraSeg family, a set of CPU-native segmentation models operating below 0.3M parameters. UltraSeg-108K (0.108M) establishes the extreme-compression frontier, while UltraSeg-130K (0.130M) integrates cross-layer lightweight fusion for enhanced multi-center generalization. The architecture replaces parameter-heavy components with grouped multi-rate dilated convolutions and attention-gated cross-layer fusion, achieving real-time throughput on a single CPU core (exceeding 50 FPS at 256*256 and 30 FPS at 352*352) without sacrificing clinical-grade accuracy. Evaluated on seven public datasets, UltraSeg-130K attains Dice scores exceeding 0.8 at both resolutions, substantially outperforming all existing sub-0.3M competitors. Notably, it approaches or exceeds UNet-Medium (7.76M parameters) on zero-shot external validations while using only 1.7% of its parameters, establishing the first strong baseline for CPU-native real-time polyp segmentation. When scaled to 4.38M parameters, UltraSeg achieves accuracy competitive with heavyweight state-of-the-art models while maintaining an order-of-magnitude parameter advantage, demonstrating that the proposed design principles yield intrinsic representational gains across the entire efficiency spectrum. By delivering the first clinically deployable, CPU-native real-time solution, this work provides an immediately usable tool for resource-limited settings and a reproducible blueprint for real-time medical AI beyond endoscopy. Source code is publicly available.
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。
