AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
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.
