基于深度学习的息肉分割技术可以更好、更快地帮助医生诊断肠壁息肉，也就是结直肠癌的前身。主流息肉分割方法是在全面监督下实施的。这些方法无法充分利用昂贵而珍贵的像素级标签，仅利用更强大的主干网络来加强特征表达而不是充分挖掘现有的息肉目标信息是一个偏差方向。为了解决这种情况，提出了多尺度网格优先和类间边界感知变压器（MGCBFormer）。 MGCBFormer 由高度可解释的组件组成：1）用于寻求最佳特征表达的多尺度网格先验和嵌套通道注意块（MGNAB），2）用于关注前景边界的类间边界感知块（CBB）和通过结合边界预处理策略充分抑制背景边界，3）合理的深度监督分支和噪声滤波器称为全局双轴关联耦合器（GDAC）。在五个公共息肉数据集（Kvasir-SEG、CVC-ClinicDB、CVC-ColonDB、CVC-300 和 ETIS-LaribPolypDB）上进行了大量有说服力的实验，与十二种息肉分割方法进行比较，并证明了其优越的预测性能和泛化能力MGCB 领先于最先进的息肉分割方法。版权所有 © 2023 Elsevier Ltd。保留所有权利。

The polyp segmentation technology based on deep learning could better and faster help doctors diagnose the polyps in the intestinal wall, which are predecessors of colorectal cancer. Mainstream polyp segmentation methods are implemented under full supervision. For these methods, expensive and precious pixel-level labels couldn't be utilized sufficiently, and it's a deviation direction to strengthen the feature expression only using the more powerful backbone network instead of fully mining existing polyp target information. To address the situation, the multiscale grid-prior and class-inter boundary-aware transformer (MGCBFormer) is proposed. MGCBFormer is composed of highly interpretable components: 1) the multiscale grid-prior and nested channel attention block (MGNAB) for seeking the optimal feature expression, 2) the class-inter boundary-aware block (CBB) for focusing on the foreground boundary and fully inhibiting the background boundary by combining the boundary preprocessing strategy, 3) reasonable deep supervision branches and noise filters called the global double-axis association coupler (GDAC). Numerous persuasive experiments are conducted on five public polyp datasets (Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS-LaribPolypDB) comparing with twelve methods of polyp segmentation, and demonstrate the superior predictive performance and generalization ability of MGCBFormer over the state-of-the-art polyp segmentation methods.Copyright © 2023 Elsevier Ltd. All rights reserved.