A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas
Author:
Funder
National Science and Technology Council
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11517-023-02875-2.pdf
Reference49 articles.
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3. Ostrom QT et al (2014) The epidemiology of glioma in adults: a “state of the science” review. Neuro Oncol 16(7):896–913
4. Komori T (2017) The 2016 WHO classification of tumours of the central nervous system: the major points of revision. Neurol Med Chir (Tokyo) 57(7):301–311
5. Delfanti RL et al (2017) Imaging correlates for the 2016 update on WHO classification of grade II/III gliomas: implications for IDH, 1p/19q and ATRX status. J Neurooncol 135(3):601–609
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