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A unified end-to-end classification model for focal liver lesions

文献类型：期刊论文

作者Zhao, Ling8; Liu, Shuaiqi5,6,7; An, Yanling4; Cai, Wenjia2,3; Li, Bing5; Wang, Shui-Hua1; Liang, Ping3; Yu, Jie3; Zhao, Jie7,8
刊名BIOMEDICAL SIGNAL PROCESSING AND CONTROL
出版日期2023-09-01
卷号86页码:9
关键词Medical image classification Contrast -enhanced ultrasound Focal liver lesions Deep learning
ISSN号1746-8094
DOI10.1016/j.bspc.2023.105260
通讯作者An, Yanling(yanling_an@126.com) ; Liang, Ping(liangping301@hotmail.com) ; Yu, Jie(jiemi301@163.com) ; Zhao, Jie(jzhao_hbu@126.com)
英文摘要Accurate 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.
WOS关键词CONTRAST-ENHANCED ULTRASOUND ; COMPUTER-AIDED DIAGNOSIS ; ATYPICAL HEPATOCELLULAR-CARCINOMA ; NODULAR HYPERPLASIA ; DIFFERENTIAL-DIAGNOSIS ; US
资助项目National 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]
WOS研究方向Engineering
语种英语
WOS记录号WOS:001147935500001
出版者ELSEVIER SCI LTD
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/55452]  
专题多模态人工智能系统全国重点实验室
通讯作者An, Yanling; Liang, Ping; Yu, Jie; Zhao, Jie
作者单位1.Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo 454000, Peoples R China
2.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth Beijing, Dept Ultrasound, Beijing, Peoples R China
3.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 5, Dept Intervent Ultrasound, Beijing 100854, Peoples R China
4.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
6.Machine Vis Engn Res Ctr Hebei Prov, Baoding 071002, Peoples R China
7.Hebei Univ, Coll Elect & Informat Engn, Baoding 071002, Peoples R China
8.Hebei Univ, Sch Qual & Tech Supervis, Baoding 071002, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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