Synthesis and classification of pulmonary nodules using two-stage-based generative adversarial network incorporating contextual transformer(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第12期
- Page:
- 1517-1531
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Synthesis and classification of pulmonary nodules using two-stage-based generative adversarial network incorporating contextual transformer
- Author(s):
- YIN Zhixian1; 4; XIA Kewen1; ZHANG Zhao2; HE Ziping3
- 1. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China 2. First Teaching Hospital of Tianjin University of Traditional Chinese Medicine/National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin 300193, China 3. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China 4. School of Software and Communication, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China
- Keywords:
- Keywords: pulmonary nodule synthesis contextual transformer generative adversarial network pulmonary nodule classification CycleGAN
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.12.009
- Abstract:
- 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.
Last Update: 2024-12-20