基于混合架构（Transformer+CNN）的息肉分割
Hybrid(Transformer+CNN)-based Polyp Segmentation
- Mississippi State University（密苏里州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对息肉大小多变及内窥镜伪影导致的分割难题，提出一种混合Transformer与CNN的模型，通过边界感知注意力机制和鲁棒特征提取，显著提升了分割准确率与抗伪影能力。
AI中文摘要：
结肠镜检查仍是检测和分割结肠息肉的主要方法，近期深度学习网络如U-Net、ResUNet、Swin-UNet和PraNet在息肉分割中取得了杰出表现。然而，由于息肉的大小、形状、内窥镜类型、光照、成像协议及边界不清（液体、褶皱）存在高度差异，准确分割仍是一项极具挑战且棘手的任务。为应对息肉分割中的这些关键挑战，我们提出一种混合模型（Transformer + CNN），旨在增强对不断变化的息肉特征的鲁棒性。我们的混合架构展现出优于现有方案的性能，尤其在解决两个关键挑战上：（1）通过边界感知注意力机制，实现对边界不清息肉的准确分割；（2）在存在常见内窥镜伪影（包括镜面高光、运动模糊和液体遮挡）时，实现鲁棒的特征提取。定量评估显示，与最先进的息肉分割方法相比，分割准确率有显著提升（Recall提升1.76%即0.9555，准确率提升0.07%即0.9849），且抗伪影能力更强。
英文摘要：
Colonoscopy is still the main method of detection and segmentation of colonic polyps, and recent advancements in deep learning networks such as U-Net, ResUNet, Swin-UNet, and PraNet have made outstanding performance in polyp segmentation. Yet, the problem is extremely challenging due to high variation in size, shape, endoscopy types, lighting, imaging protocols, and ill-defined boundaries (fluid, folds) of the polyps, rendering accurate segmentation a challenging and problematic task. To address these critical challenges in polyp segmentation, we introduce a hybrid (Transformer + CNN) model that is crafted to enhance robustness against evolving polyp characteristics. Our hybrid architecture demonstrates superior performance over existing solutions, particularly in addressing two critical challenges: (1) accurate segmentation of polyps with ill-defined margins through boundary-aware attention mechanisms, and (2) robust feature extraction in the presence of common endoscopic artifacts, including specular highlights, motion blur, and fluid occlusions. Quantitative evaluations reveal significant improvements in segmentation accuracy (Recall improved by 1.76%, i.e., 0.9555, accuracy improved by 0.07%, i.e., 0.9849) and artifact resilience compared to state-of-the-art polyp segmentation methods.
