Channel prior convolutional attention for medical image segmentation

By He-lu Huang, Zuguo Chen, Ying Zou, Ming Lu, Chaoyang Chen. Comput. Biol. Medicine, 2023

297 citations recorded by the source corpus.

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 https://github.com/Cuthbert-Huang/CPCANet.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/07444ce3c90b9490b8ba12cfe890d74a210ce077

Citations in the corpus

• SCSA: Exploring the Synergistic Effects Between Spatial and Channel Attention (2024, unknown): “However, numerous studies have shown that relying solely on the convolutional branch for feature extraction can lead to distorted gradient flows [54], resulting in the loss of critical information or redundancy of similar features [4, 17, 66, 35].”