CASIA OpenIR  > 多模态人工智能系统全国重点实验室
A unified end-to-end classification model for focal liver lesions
Zhao, Ling1; Liu, Shuaiqi2,3,4; An, Yanling5; Cai, Wenjia6,7; Li, Bing4; Wang, Shui-Hua8; Liang, Ping6; Yu, Jie6; Zhao, Jie1,2
Source PublicationBIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN1746-8094
2023-09-01
Volume86Pages:9
Corresponding AuthorAn, Yanling(yanling_an@126.com) ; Liang, Ping(liangping301@hotmail.com) ; Yu, Jie(jiemi301@163.com) ; Zhao, Jie(jzhao_hbu@126.com)
AbstractAccurate diagnosis of focal liver lesions (FLLs) plays a crucial role in patients' management, surveillance, and prognosis. Contrast-enhanced ultrasound (CEUS) as a vital diagnostic tool for FLLs still faces the challenge of image feature overlap among several FLLs. In this study, we proposed a deep learning-based model, denoted as a unified end-to-end (UEE) model, to fully capture the lesion information to achieve the classification of FLLs by adopting CEUS. We first exploited ResNet50 as the backbone to extract multi-scale features from several CEUS frames. Secondly, the hybrid attention enhancement module (HAEM) was designed to enhance the significant features with various scales. The enhanced features were then concatenated and passed into the nested feature aggregation module (NFAM) to add nonlinearity to the features with various scales. Finally, all features from different frames were averaged and fed into a Sigmoid classifier for FLL classification. The experiments are developed on a multi-center dataset which ensured diversity. The extensive experimental results revealed that the UEE model achieved 88.64 % accuracy on benign (Be) and malignant (Ma) classification, and 91.27 % accuracy on hepatocellular carcinoma (HCC) and intrahepatic cholangiocellular carcinoma (ICC) classification.
KeywordMedical image classification Contrast -enhanced ultrasound Focal liver lesions Deep learning
DOI10.1016/j.bspc.2023.105260
WOS KeywordCONTRAST-ENHANCED ULTRASOUND ; COMPUTER-AIDED DIAGNOSIS ; ATYPICAL HEPATOCELLULAR-CARCINOMA ; NODULAR HYPERPLASIA ; DIFFERENTIAL-DIAGNOSIS ; US
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[62172139] ; Natural Science Foundation of Hebei Province[F2022201055] ; Hebei University Research and Innovation Team Support Project[IT2023B05] ; Science Foundation Science Research Project of Hebei Province[BJ2020030] ; Natural Science Interdisciplinary Research Program of Hebei University[2022M713361] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[DXK202102] ; Postgraduate's Innovation Fund Project of Hebei University[202200007] ; Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology[HBU2023BS021] ; High-Performance Computing Center of Hebei University[2020GDDSIPL-04]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Hebei Province ; Hebei University Research and Innovation Team Support Project ; Science Foundation Science Research Project of Hebei Province ; Natural Science Interdisciplinary Research Program of Hebei University ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Postgraduate's Innovation Fund Project of Hebei University ; Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology ; High-Performance Computing Center of Hebei University
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:001147935500001
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55453
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorAn, Yanling; Liang, Ping; Yu, Jie; Zhao, Jie
Affiliation1.Hebei Univ, Sch Qual & Tech Supervis, Baoding 071002, Peoples R China
2.Hebei Univ, Coll Elect & Informat Engn, Baoding 071002, Peoples R China
3.Machine Vis Engn Res Ctr Hebei Prov, Baoding 071002, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
5.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
6.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 5, Dept Intervent Ultrasound, Beijing 100854, Peoples R China
7.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth Beijing, Dept Ultrasound, Beijing, Peoples R China
8.Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo 454000, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Ling,Liu, Shuaiqi,An, Yanling,et al. A unified end-to-end classification model for focal liver lesions[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2023,86:9.
APA Zhao, Ling.,Liu, Shuaiqi.,An, Yanling.,Cai, Wenjia.,Li, Bing.,...&Zhao, Jie.(2023).A unified end-to-end classification model for focal liver lesions.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,86,9.
MLA Zhao, Ling,et al."A unified end-to-end classification model for focal liver lesions".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 86(2023):9.
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