基于金字塔视觉变压器的新型适配器模块，提升息肉分割精度与速度
PVTAdpNet: Polyp Segmentation using Pyramid vision transformer with a novel Adapter block
- 融合金字塔视觉变压器与U-Net结构，引入适配器跳过连接增强特征提取
- 在跨分布数据集上达到0.8851的Dice系数和0.8167的mIoU
- 适合临床实时应用，代码开源便于复现
结直肠癌是常见且致命的癌症之一，亟需高效早期检测与治疗。为克服传统肠镜检查因息肉形态多样导致的漏诊问题，本文提出金字塔视觉变压器适配器残差网络（PVTAdpNet）。该模型结合U-Net式编码器-解码器结构、金字塔视觉变压器主干网络、新型残差块及基于适配器的跳跃连接，增强特征提取、密集预测与梯度传播能力，并引入挤压-激励注意力机制优化通道特征。PVTAdpNet实现高精度实时息肉分割，在基准数据集上表现优异，尤其在跨分布息肉数据集上取得0.8851的Dice系数和0.8167的mIoU。PolypGen数据集评估表明其在已知分布下同样具备实时准确性能。源代码已公开于https://github.com/ayousefinejad/PVTAdpNet.git。
原文摘要 · Abstract (English)
Colorectal cancer ranks among the most common and deadly cancers, emphasizing the need for effective early detection and treatment. To address the limitations of traditional colonoscopy, including high miss rates due to polyp variability, we introduce the Pyramid Vision Transformer Adapter Residual Network (PVTAdpNet). This model integrates a U-Net-style encoder-decoder structure with a Pyramid Vision Transformer backbone, novel residual blocks, and adapter-based skip connections. The design enhances feature extraction, dense prediction, and gradient flow, supported by squeeze-and-excitation attention for improved channel-wise feature refinement. PVTAdpNet achieves real-time, accurate polyp segmentation, demonstrating superior performance on benchmark datasets with high mDice and mIoU scores, making it highly suitable for clinical applications. PVTAdpNet obtains a high Dice coefficient of 0.8851 and a mean Intersection over Union (mIoU) of 0.8167 on out-of-distribution polyp datasets. Evaluation of the PolypGen dataset demonstrates PVTAdpNet's capability for real-time, accurate performance within familiar distributions. The source code of our network is available at https://github.com/ayousefinejad/PVTAdpNet.git
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