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Knowledge tensor embedding framework with association enhancement for breast ultrasound diagnosis of limited labeled samples

文献类型：期刊论文

作者Xi, Jianing5,6; Miao, Zhaoji1; Liu, Longzhong4; Yang, Xuebing2,3; Zhang, Wensheng2,3; Huang, Qinghua5,6; Li, Xuelong5,6
刊名NEUROCOMPUTING
出版日期2022-01-11
卷号468页码:60-70
关键词Breast ultrasound Knowledge graph Tensor decomposition Limited labeled samples
ISSN号0925-2312
DOI10.1016/j.neucom.2021.10.013
通讯作者Huang, Qinghua(qhhuang@nwpu.edu.cn) ; Li, Xuelong(xuelong_li@nwpu.edu.cn)
英文摘要In the AI diagnosis of breast cancer, instead of ultrasound images from non-standard acquisition process, the Breast Image Reporting and Data System (BI-RADS) reports are widely accepted as the input data since it can give standardized descriptions for the breast ultrasound samples. The BI-RADS reports are usually stored as the format of Knowledge Graph (KG) due to the flexibility, and the KG embedding is a common procedure for the AI analysis on BI-RADS data. However, since most existing embedding methods are based on the local connections in KG, in the situation of limited labeled samples, there is a clear need for embedding based diagnosis method which is capable of representing the global interactions among all entities/relations and associating the labeled/unlabeled samples. To diagnose the breast ultrasound samples with limited labels, in this paper we propose an efficient framework Knowledge Tensor Embedding with Association Enhancement Diagnosis (KTEAED), which adopts tensor decomposition into the embedding to achieve the global representation of KG entities/relations, and introduces the association enhancement strategy to prompt the similarities between embeddings of labeled/unlabeled samples. The embedding vectors are then utilized to diagnose the clinical outcomes of samples by predicting their links to outcomes entities. Through extensive experiments on BI-RADS data with different fractions of labels and ablation studies, our KTEAED displays promising performance in the situations of various fractions of labels. In summary, our framework demonstrates a clear advantage of tackling limited labeled samples of BI-RADS reports in the breast ultrasound diagnosis. (c) 2021 Elsevier B.V. All rights reserved.
WOS关键词COMPUTER-AIDED DIAGNOSIS ; QUANTITATIVE-ANALYSIS ; CLASSIFICATION ; ALIGNMENT ; LESIONS
资助项目National Key Research and Development Program of China[2018AAA0102104] ; National Natural Science Foundation of China[61901322] ; National Natural Science Foundation of China[62071382] ; China Postdoctoral Science Foundation[2020M673494] ; Innovation Capability Support Program of Shaanxi[2021TD-57] ; Shaanxi Provincial Foundation for Distinguished Young Scholars[2019JC-13]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000714774800006
出版者ELSEVIER
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Innovation Capability Support Program of Shaanxi ; Shaanxi Provincial Foundation for Distinguished Young Scholars
源URL[http://ir.ia.ac.cn/handle/173211/46485]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Huang, Qinghua; Li, Xuelong
作者单位1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China
4.Sun Yat Sen Univ, Canc Ctr, Guangzhou 510060, Peoples R China
5.Northwestern Polytech Univ, Minist Ind & Informat Technol, Key Lab Intelligent Interact & Applicat, Xian 710072, Peoples R China
6.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China
推荐引用方式
GB/T 7714		 Xi, Jianing,Miao, Zhaoji,Liu, Longzhong,et al. Knowledge tensor embedding framework with association enhancement for breast ultrasound diagnosis of limited labeled samples[J]. NEUROCOMPUTING,2022,468:60-70.
APA Xi, Jianing.,Miao, Zhaoji.,Liu, Longzhong.,Yang, Xuebing.,Zhang, Wensheng.,...&Li, Xuelong.(2022).Knowledge tensor embedding framework with association enhancement for breast ultrasound diagnosis of limited labeled samples.NEUROCOMPUTING,468,60-70.
MLA Xi, Jianing,et al."Knowledge tensor embedding framework with association enhancement for breast ultrasound diagnosis of limited labeled samples".NEUROCOMPUTING 468(2022):60-70.

入库方式： OAI收割

来源：自动化研究所

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