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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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| 主要な著者: | , , , , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2011
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| 主題: | |
| オンライン･アクセス: | 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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