HER2-ResNet: A HER2 classification method based on deep residual network.
Background: HER2 gene expression is one of the main reference indicators for breast cancer detection and treatment, and it is also an important target for tumor targeted therapy drug selection. Therefore, the correct detection and evaluation of HER2 gene expression has important value for clinical t...
| Publicado en: | Technology & Health Care Vol. 30; pp. 215 - 225 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2022 Supplement 1
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156140342&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156140342 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09287329 3QT jtl: Technology & Health Care issn: 09287329 maglogo: N pubinfo: dt: 2022 Supplement 1 vid: 30 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 156140342 156140342 NLM35124598 156140342 10.3233/THC-228020 NLM35124598 156140342 ppf: 215 ppct: 10 formats: tig: atl: HER2-ResNet: A HER2 classification method based on deep residual network. aug: au: Wang, Xingang Shao, Cuiling Liu, Wensheng Liang, Hu Li, Na affil: School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China sug: subj: Breast Neoplasms Breast Neoplasms Pathology Algorithms Female Disease Progression Scales Questionnaires Female ab: Background: HER2 gene expression is one of the main reference indicators for breast cancer detection and treatment, and it is also an important target for tumor targeted therapy drug selection. Therefore, the correct detection and evaluation of HER2 gene expression has important value for clinical treatment of breast cancer.Objective: The study goal is to better classify HER2 images.Methods: For general convolution neural network, with the increase of network layers, over fitting phenomenon is often very serious, which requires setting the value of random descent ratio, and parameter adjustment is often time-consuming and laborious, so this paper uses residual network, with the increase of network layer, the accuracy will not be reduced.Results: In this paper, a HER2 image classification algorithm based on improved residual network is proposed. Experimental results show that the proposed HER2 network has high accuracy in breast cancer assessment.Conclusion: Taking HER2 images in Stanford University database as experimental data, the accuracy of HER2 image automatic classification is improved through experiments. This method will help to reduce the detection intensity and improve the accuracy of HER2 image classification. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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