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Combining handcrafted features with latent variables in machine learning for prediction of radiation‐induced lung damage
PURPOSE: There has been burgeoning interest in applying machine learning methods for predicting radiotherapy outcomes. However, the imbalanced ratio of a large number of variables to a limited sample size in radiation oncology constitutes a major challenge. Therefore, dimensionality reduction method...
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| Wydane w: | Med Phys |
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| Główni autorzy: | , , , , |
| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
John Wiley and Sons Inc.
2019
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| Hasła przedmiotowe: | |
| Dostęp online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6510637/ https://ncbi.nlm.nih.gov/pubmed/30891794 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/mp.13497 |
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