Multiscale Time-Sharing Elastography Algorithms and Transfer Learning of Clinicopathological Features of Uterine Cervical Cancer for Medical Intelligent Computing System.
Intelligent medical diagnosis and computing system faces many challenges in complex object recognition, large-scale data imaging and real-time diagnosis, such as poor real-time computing, low efficiency of data storage and low recognition rate of lesions. In order to solve the above problems, this p...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 10 |
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| Autores principales: | , , , |
| Formato: | computer program equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2019
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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=138910959&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138910959 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2019 vid: 43 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138910959 138910959 138910959 10.1007/s10916-019-1433-z 138910959 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multiscale Time-Sharing Elastography Algorithms and Transfer Learning of Clinicopathological Features of Uterine Cervical Cancer for Medical Intelligent Computing System. aug: au: Dong, Xiaojun Du, Hongmei Guan, Haichen Zhang, Xuezhen affil: Hunan University of Medicine, 418000, Huaihua, China sug: subj: Ultrasonography Methods Algorithms Computing Methodologies Uterine Neoplasms Ultrasonography Cervix Neoplasms Ultrasonography Uterine Neoplasms Pathology Cervix Neoplasms Pathology Uterine Neoplasms Symptoms Cervix Neoplasms Symptoms Machine Learning Image Processing, Computer Assisted Signal Processing, Computer Assisted Artificial Intelligence Metadata Inferential Statistics Ultrasonography Standards Time Factors ab: Intelligent medical diagnosis and computing system faces many challenges in complex object recognition, large-scale data imaging and real-time diagnosis, such as poor real-time computing, low efficiency of data storage and low recognition rate of lesions. In order to solve the above problems, this paper proposes a medical intelligent computing system and a series of algorithms for the clinical pathology of cervical cancer based on the multi-scale imaging and transfer learning framework. Firstly, based on data dimensions, imaging errors and other factors, this paper designs a multi-scale time-sharing elastic imaging algorithm based on image reconstruction time and data sample characteristics. Then, taking the burst imaging cohort and the calculation data set of new cervical cancer cases as the objects, based on the difficulties of cervical cancer feature modeling, this paper proposes the transfer learning algorithm of clinical and pathological features of cervical cancer. Finally, a medical intelligent computing system for cervical cancer pathology analysis and calculation with high efficiency and reliability is established. A series of proposed algorithms are compared with single-scale Retinex (SSR), which is based on single-scale Retinex migration learning (SSR-TL). The experimental results show that the proposed algorithm in cervical cancer pathological imaging and scoring, as well as the feature extraction and recognition of lesions, especially the efficiency of system execution, is obviously due to the comparison algorithm. pubtype: Academic Journal doctype: computer program equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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