Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images.
The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/26/2016
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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=119085681&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119085681 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/26/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 119085681 119085681 119085681 10.1155/2016/3025057 119085681 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images. aug: au: Joutsijoki, Henry Haponen, Markus Rasku, Jyrki Aalto-Setälä, Katriina Juhola, Martti affil: School of Information Sciences, University of Tampere, Kanslerinrinne 1, 33014 Tampere, Finland sug: subj: Stem Cells Classification Coding Descriptive Statistics Probability Validity Discriminant Analysis Comparative Studies Microscopy Algorithms Funding Source ab: The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of them k-Nearest Neighbor (k-NN) searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use Scaled Invariant Feature Transformation (SIFT) based features in classification. The best accuracy (62.4%) is obtained with ternary complete ECOC coding design and k-NN classifier (standardized Euclidean distance measure and inverse weighting). The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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