Channel prior convolutional attention for medical image segmentation
Hejun Huang Zuguo Chen Ying Zou Ming Lu Chaoyang Chen
Characteristics such as low contrast and significant organ shape variations
are often exhibited in medical images. The improvement of segmentation
performance in medical imaging is limited by the generally insufficient
adaptive capabilities of existing attention mechanisms. An efficient Channel
Prior Convolutional Attention (CPCA) method is proposed in this paper,
supporting the dynamic distribution of attention weights in both channel and
spatial dimensions. Spatial relationships are effectively extracted while
preserving the channel prior by employing a multi-scale depth-wise
convolutional module. The ability to focus on informative channels and
important regions is possessed by CPCA. A segmentation network called CPCANet
for medical image segmentation is proposed based on CPCA. CPCANet is validated
on two publicly available datasets. Improved segmentation performance is
achieved by CPCANet while requiring fewer computational resources through
comparisons with state-of-the-art algorithms. Our code is publicly available at
\url{https://github.com/Cuthbert-Huang/CPCANet}.