LMA-Net: Lightweight Multiple Attention Network for Multi-Source Heterogeneous Pulmonary CXR Segmentation

Abstract

The automatic pulmonary segmentation for chest X-ray(CXR) plays an important role in assisting diagnosis. Many deep learning methods have the problems of high computational complexity and low segmentation accuracy, which hinder the application to clinical workstations. Therefore, this paper proposes a lightweight multiple attention network(LMA-Net), which improved U-Net by using the progressive dilated convolution(PDC) for lightweight. A reinforced channel attention(RCA) and a multiscale attention(MSA) are embedded in the decoder to further improve the network segmentation performance. We fuse four types of pulmonary disease CXR from the COVID-QE-Ex dataset to generate a multi-source heterogeneous dataset. Effectiveness of LMA-Net is shown by achieving Intersection over Union(IoU ) of 96.28%, Dice of 96.95%, Average symmetric surface distance(ASSD) of 13.11mm and Hausdorff Distance 95th percentile(HD95) of 81.12mm, respectively. It can be seen that lightweight of LMA-Net is achieved according to parameter(Param) of 2.89M and floating-point operations(FLOPs) of 2.64G. This method can effectively improve segmentation performance and speed.