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Accurate and Interpretable Classification of Microspectroscopy Pixels Using Artificial Neural Networks
This paper addresses the problem of classifying materials from microspectroscopy at a pixel level. The challenges lie in identifying discriminatory spectral features and obtaining accurate and interpretable models relating spectra and class labels. We approach the problem by designing a supervised c...
Tallennettuna:
| Julkaisussa: | Med Image Anal |
|---|---|
| Päätekijät: | , , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
2017
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5500246/ https://ncbi.nlm.nih.gov/pubmed/28131075 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.media.2017.01.001 |
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