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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...
Αποθηκεύτηκε σε:
| Τόπος έκδοσης: | Proc IEEE Int Symp Biomed Imaging |
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| Κύριοι συγγραφείς: | , , , , , , , |
| Μορφή: | Artigo |
| Γλώσσα: | Inglês |
| Έκδοση: |
2019
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| Θέματα: | |
| Διαθέσιμο Online: | 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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