A plug-and-play attention module for CT-based COVID-19 segmentation

ABSTRACT

At the end of 2019, a new type of coronavirus (COVID-19) rapidly spread globally, even if the penetration of vaccination is getting higher and higher, the emergence of viral variants has increased the number of new coronal pneumonia infections. The deep learning model can help doctors quickly and accurately divide the lesion zone. However, there are many problems in the segmentation of the slice from the CT slice, including the problem of uncertainty of the disease area, low accuracy. At the same time, the semantic segmentation model of the traditional CNN architecture has natural defects, and the sensing field restrictions result in constructing the relationship between pixels and pixels, and the context information is insufficient. In order to solve the above problems, we introduced a Transformer module. Visual Transformer has been proved to effectively improve the accuracy of the model. We have designed a plug-and-play spatial attention module, on the basis of attention, increased positional offset, effective aggregate advanced features, and improve the accuracy of existing models. © 2021 Institute of Physics Publishing. All rights reserved.