Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network.
The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily cl...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 6; pp. 1187 - 1199 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2019
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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=136505520&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136505520 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2019 vid: 57 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136505520 136505520 NLM30687900 10.1007/s11517-018-01946-z NLM30687900 136505520 ppf: 1187 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network. aug: au: Zhang, XiaoQing Zhao, Shu-Guang affil: College of Information Science and Technology, Donghua University, 201620, Shanghai, China sug: subj: Neural Networks (Computer) Microscopy Methods Cells Image Processing, Computer Assisted Muscle Proteins Metabolism Scales ab: The development of computer technology now allows the quick and efficient automatic fluorescence microscopy generation of a large number of images of proteins in specific subcellular compartments using fluorescence microscopy. Digital image processing and pattern recognition technology can easily classify these images, identify the subcellular location of proteins, and subsequently carry out related work such as analysis and investigation of protein function. Here, based on a fluorescence microscopy 2D image dataset of HeLa cells, the CapsNet network model was used to classify ten types of images of proteins in different subcellular compartments. Capsules in the CapsNet network model were trained to capture the possibility of certain features and variants rather than to capture the characteristics of a specific variant. The capsule at the same level predicted the instantiation parameters of the higher level capsule through the transformation matrix, and the higher level capsule became active when multiple dynamic routing forecasts were consistent. Experiments show that using the CapsNet network model to classify 2D HeLa datasets can achieve higher accuracy. Graphical abstract ᅟ. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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