Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation.

ABSTRACT: Segmentation of COVID-19 infection is a challenging task due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, especially for small infection regions. COV-TransNet is presented to achieve high-precision segmentation of COVID-19 infection regions in this paper. The proposed segmentation network is composed of the auxiliary branch and the backbone branch. The auxiliary branch network adopts transformer to provide global information, helping the convolution layers in backbone branch to learn specific local features better. A multi-scale feature attention module is introduced to capture contextual information and adaptively enhance feature representations. Specially, a high internal resolution is maintained during the attention ca

SUBMITTER: Peng Y 

PROVIDER: S-EPMC9671472 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature