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FeAture Explorer (FAE): A tool for developing and comparing radiomics models
In radiomics studies, researchers usually need to develop a supervised machine learning model to map image features onto the clinical conclusion. A classical machine learning pipeline consists of several steps, including normalization, feature selection, and classification. It is often tedious to fi...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431107/ https://www.ncbi.nlm.nih.gov/pubmed/32804986 http://dx.doi.org/10.1371/journal.pone.0237587 |