Synthesis and classification of pulmonary nodules using two-stage-based generative adversarial network incorporating contextual transformer
10.3969/j.issn.1005-202X.2024.12.009
- VernacularTitle:融合上下文注意力的两段式生成对抗网络的肺结节图像生成与分类
- Author:
Zhixian YIN
1
;
Kewen XIA
;
Zhao ZHANG
;
Ziping HE
Author Information
1. 河北工业大学电子信息工程学院,天津 300401;天津中德应用技术大学软件与通信学院,天津 300350
- Publication Type:Journal Article
- Keywords:
pulmonary nodule synthesis;
contextual transformer;
generative adversarial network;
pulmonary nodule classification;
CycleGAN
- From:
Chinese Journal of Medical Physics
2024;41(12):1517-1531
- CountryChina
- Language:Chinese
-
Abstract:
A two-stage-based generative adversarial network incorporating contextual transformer is proposed for synthesis and multiclass classification of pulmonary nodules.Contextual transformer adopts a channel-enhanced multi-head contextual transformer mechanism which combines channel attention and multi-head contextual transformer to better deal with the complex semantic relationship in the feature space,thereby effectively enhancing the feature extraction capability of the model.A two-stage-based generative adversarial network framework is used to achieve the injection of pulmonary nodules in the designated lung area,and divide the synthesis task into two stages.In the first stage,pulmonary nodule regions of interest images are generated and then fused with designated lung parenchyma through a Poisson blending module to generate preliminary samples;in the second stage,an improved CycleGAN model is used to fine-tune the preliminary samples.Meanwhile,the skip layer excitation module and auxiliary classifier are introduced into the discriminator for realizing the re-correction of the feature channel and the classification of pulmonary nodules.Experiments on LIDC-IDRI dataset reveal that the proposed method has a FID,IS and KID of 115.153,2.619±0.095 and 0.062 on pulmonary nodule synthesis,and achieves an accuracy,sensitivity,F1 value and AUC of 70.23%,68.66%,68.92%and 87.59%on pulmonary nodule malignancy classification,respectively,outperforming GAN-based classification models such as ADGAN,as well as benchmark networks such as VGG16.
