A Pyramid Architecture-Based Deep Learning Framework for Breast Cancer Detection.
Breast cancer diagnosis is a critical step in clinical decision making, and this is achieved by making a pathological slide and gives a decision by the doctors, which is the method of final decision making for cancer diagnosis. Traditionally, the doctors usually check the pathological images by visu...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/1/2021
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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=152765630&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152765630 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/1/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 152765630 152765630 152765630 10.1155/2021/2567202 152765630 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Pyramid Architecture-Based Deep Learning Framework for Breast Cancer Detection. aug: au: Sui, Dong Liu, Weifeng Chen, Jing Zhao, Chunxiao Ma, Xiaoxuan Guo, Maozu Tian, Zhaofeng affil: School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China sug: subj: Deep Learning Conceptual Framework Breast Neoplasms Diagnosis Early Detection of Cancer Health Information Systems Decision Making, Clinical Diagnostic Imaging Inspection (Clinical) Methods Microscopy, Virtual Time Factors Image Processing, Computer Assisted Methods Protocols Outcome Assessment Sequence Analysis Validity Slides Inspection (Clinical) Models, Educational Human Female Descriptive Statistics Data Analysis Software Female ab: Breast cancer diagnosis is a critical step in clinical decision making, and this is achieved by making a pathological slide and gives a decision by the doctors, which is the method of final decision making for cancer diagnosis. Traditionally, the doctors usually check the pathological images by visual inspection under the microscope. Whole-slide images (WSIs) have supported the state-of-the-art diagnosis results and have been admitted as the gold standard clinically. However, this task is time-consuming and labour-intensive, and all of these limitations make low efficiency in decision making. Medical image processing protocols have been used for this task during the last decades and have obtained satisfactory results under some conditions; especially in the deep learning era, it has exhibited the advantages than those in the shallow learning period. In this paper, we proposed a novel breast cancer region mining framework based on deep pyramid architecture from multilevel and multiscale breast pathological WSIs. We incorporate the tissue- and cell-level information together and integrate these into a LSTM model for the final sequence modelling, which successfully keeps the WSIs' integration and is not mentioned by the prevalence frameworks. The experiment results demonstrated that our proposed framework greatly improved the detection accuracy than that only using tissue-level information. 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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