COLLABORATIVE CLUSTERING OF SUBJECTS AND RADIOMIC FEATURES FOR PREDICTING CLINICAL OUTCOMES OF RECTAL CANCER PATIENTS

Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adop...

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Detalles Bibliográficos
Publicado en:Proc IEEE Int Symp Biomed Imaging
Main Authors: Liu, Hangfan, Li, Hongming, Boimel, Pamela, Janopaul-Naylor, James, Zhong, Haoyu, Xiao, Ying, Ben-Josef, Edgar, Fan, Yong
Formato: Artigo
Idioma:Inglês
Publicado: 2019
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Article
Acceso en liña:https://ncbi.nlm.nih.gov/pmc/articles/PMC6892162/
https://ncbi.nlm.nih.gov/pubmed/31803347
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/ISBI.2019.8759512
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