COMPARISON OF SPARSE CODING AND KERNEL METHODS FOR HISTOPATHOLOGICAL CLASSIFICATION OF GLIOBASTOMA MULTIFORME

This paper compares performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven to be an effective technique for restoration, and has recently been extended to classification. The main issue with clas...

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Hauptverfasser: Han, Ju, Chang, Hang, Loss, Leandro, Zhang, Kai, Baehner, Fredrick L., Gray, Joe W., Spellman, Paul, Parvin, Bahram
Format: Artigo
Sprache:Inglês
Veröffentlicht: 2011
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Article
Online Zugang:https://ncbi.nlm.nih.gov/pmc/articles/PMC3521607/
https://ncbi.nlm.nih.gov/pubmed/23243485
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/ISBI.2011.5872505
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