4 citations · 11 across the 6 of their papers we have counts for
6 papers
Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices
Joseph Stember, Mehrnaz Jenabi, Luca Pasquini +3
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 eac…
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
Joseph Stember, Danielle Stember, Luca Pasquini +3
Purpose : Because functional MRI (fMRI) data sets are in general small, we sought a data efficient approach to resting state fMRI classification of autism spectrum disorder (ASD) v…
Deep learning can differentiate IDH-mutant from IDH-wild type GBM
Luca Pasquini, Antonio Napolitano, Emanuela Tagliente +10
Background: Distinction of IDH mutant and wildtype GBMs is challenging on MRI, since conventional imaging shows considerable overlap. While few studies employed deep-learning in a…
Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation
Luca Pasquini, Antonio Napolitano, Martina Lucignani +11
Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM). However, clinical implementation is limited by lack of parameters standard…
Monolingual and bilingual language networks in healthy subjects using functional MRI and graph theory
Qiongge Li, Luca Pasquini, Gino Del Ferraro +4
Pre-surgical language mapping with functional magnetic resonance imaging (fMRI) is routinely conducted to assist the neurosurgeon in preventing damage to brain regions responsible…
Core language brain network for fMRI-language task used in clinical applications
Qiongge Li, Gino Del Ferraro, Luca Pasquini +3
Functional magnetic resonance imaging (fMRI) is widely used in clinical applications to highlight brain areas involved in specific cognitive processes. Brain impairments, such as t…