https://scholars.lib.ntu.edu.tw/handle/123456789/628909
標題: | An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification | 作者: | Huang, Yao-Sian Wang, Teh-Chen Huang, Sheng-Zhi Zhang, Jun HSIN-MING CHEN YEUN-CHUNG CHANG RUEY-FENG CHANG |
關鍵字: | Attention mechanism; Computer-aided diagnosis; Feature pyramid network; Hybrid loss; Lung nodules; Residual network | 公開日期: | 二月-2023 | 出版社: | ELSEVIER IRELAND LTD | 卷: | 229 | 來源出版物: | Computer methods and programs in biomedicine | 摘要: | Lung cancer has the highest cancer-related mortality worldwide, and lung nodule usually presents with no symptom. Low-dose computed tomography (LDCT) was an important tool for lung cancer detection and diagnosis. It provided a complete three-dimensional (3-D) chest image with a high resolution.Recently, convolutional neural network (CNN) had flourished and been proven the CNN-based computer-aided diagnosis (CADx) system could extract the features and help radiologists to make a preliminary diagnosis. Therefore, a 3-D ResNeXt-based CADx system was proposed to assist radiologists for diagnosis in this study. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/628909 | ISSN: | 0169-2607 | DOI: | 10.1016/j.cmpb.2022.107278 |
顯示於: | 醫學院附設醫院 (臺大醫院) |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。