Search Results for author:
Found 4 papers, 0 papers with code
Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices
Whereas MRI produces anatomic information about the brain, functional MRI (fMRI) tells us about neural activity within the brain, including how various regions communicate with each other.
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
We hypothesized that a Deep Reinforcement Learning (DRL) classifier could learn effectively on a small fMRI training set.
Deep learning can differentiate IDH-mutant from IDH-wild type GBM
Conclusion: We built a GBM-tailored deep-learning model for IDH mutation prediction, achieving accuracy of 83% with rCBV maps.
Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation
We aimed to compare nine machine learning classifiers, with different optimization parameters, to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients, based on radiomic features from conventional and advanced MR. 156 adult patients with pathologic diagnosis of GBM were included.
