四川大学学报（自然科学版）2024，Vol.61Issue(6)：49-58,10.DOI:10.19907/j.0490-6756.2024.062001
DNAS-Net:一种用于肺炎分割的新型密集嵌套网状编码器
DNAS-Net:A novel dense nested anastomosing encoder for pneumonia segmentation
摘要
Abstract
Since the emergence of the COVID-19 coronavirus in 2019,there has been a growing interest in the detection and treatment of pneumonia.Computer-aided screening utilizing deep learning has emerged as a promising tool to enhance the accuracy of pneumonia screening and clinical diagnosis.However,traditional deep learning methods have shown limitations in effectively analyzing medical image datasets due to signifi-cant differences in the shape,size,and location of lesions in medical images.We propose a Dense Nested Anastomosing Pneumonia Segmentation model(DNAS-Net),as a novel and efficient method for segment-ing pneumonia regions in CT images.The model incorporates a dense nested anastomosing encoder that uti-lizes two hierarchical pyramid modules,namely the Attentive Hierarchical Spatial Pyramid module(AHSP)and the Attentive Separable Feature Pyramid module(ASFP),for efficient deep feature extraction.Skip connections are utilized to connect encoders and decoders from corresponding stages and a Dense Skip Fea-ture Fusion module(DSFF)is employed to bridge the semantic gap between low-level and high-level fea-tures to enhance semantic segmentation.Extensive experimental results demonstrate that the proposed DNAS-Net achieves higher segmentation accuracy.关键词
深度学习/图像分割/肺炎/多尺度/跳跃连接Key words
Deep learning/Image segmentation/Pneumonia/Multi-scale/Skip-connections引用本文复制引用
刘庭江,周凯,章毅,徐修远..DNAS-Net:一种用于肺炎分割的新型密集嵌套网状编码器[J].四川大学学报（自然科学版）,2024,61(6):49-58,10.基金项目
国家自然科学基金(62106163) （62106163）
四川省科技厅重大科技项目(2020YFG0473) （2020YFG0473）
四川省自然科学基金项目(23ZDYF0623) （23ZDYF0623）
