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...

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Detalles Bibliográficos
Autores principales: Song, Yang, Zhang, Jing, Zhang, Yu-dong, Hou, Ying, Yan, Xu, Wang, Yida, Zhou, Minxiong, Yao, Ye-feng, Yang, Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
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

Internet

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

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