Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are critical in medical image segmentation. To alleviate this problem, we propose a Dual-Aggregation Transformer Network called DuAT, which is characterized by two innovative designs, namely, the Global-to-Local Spatial Aggregation (GLSA) and Selective Boundary Aggregation (SBA) modules. The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively. The SBA module is used to aggregate the boundary characteristic from low-level features and semantic information from high-level features for better preserving boundary details and locating the re-calibration objects. Extensive experiments in six benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods in the segmentation of skin lesion images, and polyps in colonoscopy images. In addition, our approach is more robust than existing methods in various challenging situations such as small object segmentation and ambiguous object boundaries.
翻译:为缓解这一问题,我们提议建立一个名为DuAT的双重聚合变异器网络,其特点是有两种创新设计,即全球到地方空间聚合(GLSA)和选择性边界聚合(SA)模块。GLSA有能力汇总和代表全球和地方空间特征,这些特征分别有利于寻找大小物体,有利于分别定位大小物体;为缓解这一问题,我们提议建立一个称为DuAT的双重聚合变异器网络,其特点是两个创新设计,即全球到地方空间聚合(GLSA)和选择性边界聚合(SB)模块。GLSA有能力综合和代表全球和地方空间特征,这些特征往往被大模式特征的特征导致当地细节(如边界和小物体)的丢失,而这些特征对医疗图像截图至关重要;为缓解这一问题,我们提议了一个称为DuAT的双重聚合变异异变变变器网络,其特点是两个创新设计,即全球到地方的空间聚合(GLSA)模块和选择性边界聚合(SB)模块。在色皮肤图像的分块、以及结心室镜像中的聚点和聚点等方面多点空间图像中,它们都有利于定位。此外,我们的方法比更强有力,在甚的边界状况和模中更稳健的方法。