  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.
