Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images
文献类型:期刊论文
作者 | Huang, Yingying7,8,9; Si, Yang4,5,6; Hu, Bingliang7; Zhang, Yan2,3; Wu, Shuang2,3; Wu, Dongsheng1,2,3; Wang, Quan7,9 |
刊名 | COMPUTERS IN BIOLOGY AND MEDICINE |
出版日期 | 2022-11 |
卷号 | 150 |
ISSN号 | 0010-4825;1879-0534 |
关键词 | Transformer-based factorized encoder 3D convolutional autoencoder Intra-slice interaction Inter-slice interaction |
DOI | 10.1016/j.compbiomed.2022.106137 |
产权排序 | 1 |
英文摘要 | In the past decade, deep learning methods have been implemented in the medical image fields and have achieved good performance. Recently, deep learning algorithms have been successful in the evaluation of diagnosis on lung images. Although chest radiography (CR) is the standard data modality for diagnosing pneumoconiosis, computed tomography (CT) typically provides more details of the lesions in the lung. Thus, a transformer-based factorized encoder (TBFE) was proposed and first applied for the classification of pneumoconiosis depicted on 3D CT images. Specifically, a factorized encoder consists of two transformer encoders. The first transformer encoder enables the interaction of intra-slice by encoding feature maps from the same slice of CT. The second transformer encoder explores the inter-slice interaction by encoding feature maps from different slices. In addition, the lack of grading standards on CT for labeling the pneumoconiosis lesions. Thus, an acknowledged CR-based grading system was applied to mark the corresponding pneumoconiosis CT stage. Then, we pre-trained the 3D convolutional autoencoder on the public LIDC-IDRI dataset and fixed the parameters of the last convolutional layer of the encoder to extract CT feature maps with underlying spatial structural information from our 3D CT dataset. Experimental results demonstrated the superiority of the TBFE over other 3D-CNN networks, achieving an accuracy of 97.06%, a recall of 89.33%, precision of 90%, and an F1-score of 93.33%, using 10-fold cross-validation. |
语种 | 英语 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
WOS记录号 | WOS:000878510400003 |
源URL | [http://ir.opt.ac.cn/handle/181661/96235] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Wu, Dongsheng; Wang, Quan |
作者单位 | 1.Sichuan Univ, Res Ctr Artificial Intelligence Med, West China PUMC CC Chen Inst Hlth, Chengdu, Sichuan, Peoples R China 2.Sichuan Univ, West China Hosp 4, Chengdu, Sichuan, Peoples R China 3.Sichuan Univ, West China Sch Publ Hlth, Dept Radiol, Chengdu, Sichuan, Peoples R China 4.Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China 5.Sichuan Prov Peoples Hosp, Dept Neurol, Chengdu, Sichuan, Peoples R China 6.Sichuan Acad Med Sci, China, Sichuan, Peoples R China 7.Key Lab Biomed Spect, Xian 710119, Shanxi, Peoples R China 8.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 9.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Shanxi, Peoples R China |
推荐引用方式 GB/T 7714 | Huang, Yingying,Si, Yang,Hu, Bingliang,et al. Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2022,150. |
APA | Huang, Yingying.,Si, Yang.,Hu, Bingliang.,Zhang, Yan.,Wu, Shuang.,...&Wang, Quan.(2022).Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images.COMPUTERS IN BIOLOGY AND MEDICINE,150. |
MLA | Huang, Yingying,et al."Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images".COMPUTERS IN BIOLOGY AND MEDICINE 150(2022). |
入库方式: OAI收割
来源:西安光学精密机械研究所
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