DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
This work addresses the critical need for preserving boundaries and small objects in medical image segmentation, offering a domain-specific improvement for healthcare applications.
The paper tackles the problem of local detail loss in transformer-based medical image segmentation by proposing DuAT, a dual-aggregation transformer network with GLSA and SBA modules, which outperforms state-of-the-art methods on six benchmark datasets for skin lesion and polyp segmentation.
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.