Research Progress of Gliomas in Machine Learning
In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining...
Enregistré dans:
Auteurs principaux: | Yameng Wu, Yu Guo, Jun Ma, Yu Sa, Qifeng Li, Ning Zhang |
---|---|
Format: | article |
Langue: | EN |
Publié: |
MDPI AG
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/fda032d5ca40407d805d0a84a6287f7a |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?
par: Luca Pasquini, et autres
Publié: (2021) -
Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features
par: Linlin Bo, et autres
Publié: (2021) -
Multiparametric MRI Radiomics for the Early Prediction of Response to Chemoradiotherapy in Patients With Postoperative Residual Gliomas: An Initial Study
par: Zhaotao Zhang, et autres
Publié: (2021) -
Cilium Expression Score Predicts Glioma Survival
par: Srinivas Rajagopalan, et autres
Publié: (2021) -
Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma
par: Ankang Gao, et autres
Publié: (2021)