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BgNet: Classification of benign and malignant tumors with MRI multi-plane attention learning

文献类型：期刊论文

作者Liu, Hong6; Jiao, Meng-Lei5,6; Xing, Xiao-Ying4; Ou-Yang, Han-Qiang1,2,3; Yuan, Yuan4; Liu, Jian-Fang4; Li, Yuan4; Wang, Chun-Jie4; Lang, Ning4; Qian, Yue-Liang6
刊名FRONTIERS IN ONCOLOGY
出版日期2022-10-27
卷号12页码:12
ISSN号2234-943X
关键词tumor classification deep learning multi-plane fusion benign malignant
DOI10.3389/fonc.2022.971871
英文摘要ObjectivesTo propose a deep learning-based classification framework, which can carry out patient-level benign and malignant tumors classification according to the patient's multi-plane images and clinical information. MethodsA total of 430 cases of spinal tumor, including axial and sagittal plane images by MRI, of which 297 cases for training (14072 images), and 133 cases for testing (6161 images) were included. Based on the bipartite graph and attention learning, this study proposed a multi-plane attention learning framework, BgNet, for benign and malignant tumor diagnosis. In a bipartite graph structure, the tumor area in each plane is used as the vertex of the graph, and the matching between different planes is used as the edge of the graph. The tumor areas from different plane images are spliced at the input layer. And based on the convolutional neural network ResNet and visual attention learning model Swin-Transformer, this study proposed a feature fusion model named ResNetST for combining both global and local information to extract the correlation features of multiple planes. The proposed BgNet consists of five modules including a multi-plane fusion module based on the bipartite graph, input layer fusion module, feature layer fusion module, decision layer fusion module, and output module. These modules are respectively used for multi-level fusion of patient multi-plane image data to realize the comprehensive diagnosis of benign and malignant tumors at the patient level. ResultsThe accuracy (ACC: 79.7%) of the proposed BgNet with multi-plane was higher than that with a single plane, and higher than or equal to the four doctors' ACC (D1: 70.7%, p=0.219; D2: 54.1%, p<0.005; D3: 79.7%, p=0.006; D4: 72.9%, p=0.178). Moreover, the diagnostic accuracy and speed of doctors can be further improved with the aid of BgNet, the ACC of D1, D2, D3, and D4 improved by 4.5%, 21.8%, 0.8%, and 3.8%, respectively. ConclusionsThe proposed deep learning framework BgNet can classify benign and malignant tumors effectively, and can help doctors improve their diagnostic efficiency and accuracy. The code is available at https://github.com/research-med/BgNet.
资助项目Beijing Natural Science Foundation ; National Natural Science Foundation of China ; Capital's Funds for Health Improvement and Research ; [Z190020] ; [62276250] ; [82171927] ; [81971578] ; [2020-4-40916]
WOS研究方向Oncology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000885875900001
源URL[http://119.78.100.204/handle/2XEOYT63/19888]  
专题中国科学院计算技术研究所期刊论文
通讯作者Liu, Hong; Jiang, Liang; Yuan, Hui-Shu
作者单位1.Beijing Key Lab Spinal Dis Res, Beijing, Peoples R China
2.Engn Res Ctr Bone & Joint Precis Med, Beijing, Peoples R China
3.Peking Univ Third Hosp, Dept Orthopaed, Beijing, Peoples R China
4.Peking Univ Third Hosp, Dept Radiol, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Hong,Jiao, Meng-Lei,Xing, Xiao-Ying,et al. BgNet: Classification of benign and malignant tumors with MRI multi-plane attention learning[J]. FRONTIERS IN ONCOLOGY,2022,12:12.
APA Liu, Hong.,Jiao, Meng-Lei.,Xing, Xiao-Ying.,Ou-Yang, Han-Qiang.,Yuan, Yuan.,...&Wang, Xiang-Dong.(2022).BgNet: Classification of benign and malignant tumors with MRI multi-plane attention learning.FRONTIERS IN ONCOLOGY,12,12.
MLA Liu, Hong,et al."BgNet: Classification of benign and malignant tumors with MRI multi-plane attention learning".FRONTIERS IN ONCOLOGY 12(2022):12.

入库方式： OAI收割

来源：计算技术研究所

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