AgentPolyp：基于图像增强智能体的精准息肉分割
AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent
- School of Mathematics, Shandong University（山东大学数学学院）
- School of Information Science and Engineering, Shandong Normal University（山东师范大学信息科学与工程学院）
- Yeshiva University（叶史瓦大学）
- School of cyber science and technology, Sun Yat-sen University（中山大学网络空间安全学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对息肉图像退化问题，提出AgentPolyp框架，通过CLIP语义引导和强化学习动态增强图像质量，结合轻量级网络分割，实现精准息肉分割并支持模块化扩展。
AI中文摘要：
由于人为和环境因素的干扰，采集的息肉图像通常存在光线昏暗、模糊和过曝等问题，给下游息肉分割任务带来挑战。为解决息肉图像中噪声导致的退化问题，我们提出AgentPolyp，这是一种集成基于CLIP的语义引导、动态图像增强与轻量级神经网络分割的新型框架。该智能体首先通过CLIP驱动的语义分析评估图像质量（例如识别“具有血管纹理的低对比度息肉”），并采用强化学习策略动态应用多模态增强操作（例如去噪、对比度调整）。质量评估反馈循环以协作方式优化像素级增强和分割焦点，确保神经网络分割前的鲁棒预处理。这种模块化架构支持各类增强算法和分割网络的即插即用扩展，满足内窥镜设备的部署需求。
英文摘要：
Since human and environmental factors interfere, captured polyp images usually suffer from issues such as dim lighting, blur, and overexposure, which pose challenges for downstream polyp segmentation tasks. To address the challenges of noise-induced degradation in polyp images, we present AgentPolyp, a novel framework integrating CLIP-based semantic guidance and dynamic image enhancement with a lightweight neural network for segmentation. The agent first evaluates image quality using CLIP-driven semantic analysis (e.g., identifying ``low-contrast polyps with vascular textures") and adapts reinforcement learning strategies to dynamically apply multi-modal enhancement operations (e.g., denoising, contrast adjustment). A quality assessment feedback loop optimizes pixel-level enhancement and segmentation focus in a collaborative manner, ensuring robust preprocessing before neural network segmentation. This modular architecture supports plug-and-play extensions for various enhancement algorithms and segmentation networks, meeting deployment requirements for endoscopic devices.
