Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation.

ABSTRACT: Deep learning based medical image segmentation is an important step within diagnosis, which relies strongly on capturing sufficient spatial context without requiring too complex models that are hard to train with limited labelled data. Training data is in particular scarce for segmenting infection regions of CT images of COVID-19 patients. Attention models help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent criss-cross-attention module aims to approximate global self-attention while remaining memory and time efficient by separating horizontal and vertical self-similarity computations. However, capturing attention from all non-local locations can adversely impact the accuracy of semantic segmentation networks. We propose a new Dynamic

SUBMITTER: Rajamani KT 

PROVIDER: S-EPMC9246608 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature