SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images

作者：

Highlights：

• We propose a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net) for segmentation of COVID-19 lung opacification from CT images and achieves state-of-the-art performance.

• We use the attention mechanism so that the neural network can generate attention maps without external region of interest (ROI) supervision, increasing the interpretability of the neural network.

• The generalization ability and compatibility of the proposed SCOAT-Net are validated on two external datasets, showing that the proposed model has specific data migration capability.

摘要

•We propose a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net) for segmentation of COVID-19 lung opacification from CT images and achieves state-of-the-art performance.•We use the attention mechanism so that the neural network can generate attention maps without external region of interest (ROI) supervision, increasing the interpretability of the neural network.•The generalization ability and compatibility of the proposed SCOAT-Net are validated on two external datasets, showing that the proposed model has specific data migration capability.

论文关键词：COVID-19,Convolutional neural network,Segmentation,Lung opacification,Attention mechanism

论文评审过程：Received 2 February 2021, Revised 7 May 2021, Accepted 9 June 2021, Available online 10 June 2021, Version of Record 22 June 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108109