Junfeng Lu

TL;DR: This study proposes a multi-channel architecture of 3D convolutional neural networks (CNNs) for deep learning upon those metric maps, from which high-level predictive features are extracted for each individual patch of these maps.

TL;DR: This study demonstrates the potential utility of combining awake craniotomy with iMRI; it is safe and reliable to perform awake surgery using a movable iMRI.

TL;DR: R-fMRI sensitivity and specificity are high for localizing hand motor area and even equivalent or slightly higher compared with T-f MRI, given its convenience for patients.

TL;DR: A new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction, achieves the highest OS prediction accuracy compared to other state-of-the-art methods.

TL;DR: An automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI-based pre-surgical functional mapping is developed and successfully applied to brain tumor patients.