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Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whol...
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| Pubblicato in: | Nat Commun |
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| Autori principali: | , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Nature Publishing Group
2016
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4990706/ https://ncbi.nlm.nih.gov/pubmed/27527408 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/ncomms12474 |
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