Attention-augmented U-Net (AA-U-Net) for semantic segmentation.

ABSTRACT: Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to help gather contextual information within deep networks and benefit semantic segmentation tasks. The recent attention-augmented convolution model aims to capture long range interactions by concatenating self-attention and convolution feature maps. This work proposes a novel attention-augmented convolution U-Net (AA-U-Net) that enables a more accurate spatial aggregation of contextual information by integrating attention-augmented convolution

SUBMITTER: Rajamani KT 

PROVIDER: S-EPMC9311338 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature