J
Junfeng Lu
Researcher at Fudan University
Publications - 47
Citations - 985
Junfeng Lu is an academic researcher from Fudan University. The author has contributed to research in topics: Glioma & Resting state fMRI. The author has an hindex of 15, co-authored 47 publications receiving 567 citations.
Papers
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Journal ArticleDOI
Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages
Dong Nie,Junfeng Lu,Han Zhang,Ehsan Adeli,Jun Wang,Zhengda Yu,Luyan Liu,Qian Wang,Jinsong Wu,Dinggang Shen,Dinggang Shen +10 more
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.
Journal ArticleDOI
Awake language mapping and 3-Tesla intraoperative MRI-guided volumetric resection for gliomas in language areas
Junfeng Lu,Jinsong Wu,Chengjun Yao,Dongxiao Zhuang,Tianming Qiu,Xiaobing Hu,Jie Zhang,Xiu Gong,Weimin Liang,Ying Mao,Liangfu Zhou +10 more
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.
Journal ArticleDOI
Localizing hand motor area using resting-state fMRI: validated with direct cortical stimulation.
Tianming Qiu,Chao-Gan Yan,Chao-Gan Yan,Weijun Tang,Jinsong Wu,Dongxiao Zhuang,Chengjun Yao,Junfeng Lu,Fengping Zhu,Ying Mao,Liangfu Zhou +10 more
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.
Journal ArticleDOI
Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients
Zhenyu Tang,Yuyun Xu,Lei Jin,Abudumijiti Aibaidula,Junfeng Lu,Zhicheng Jiao,Jinsong Wu,Han Zhang,Dinggang Shen +8 more
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.
Journal ArticleDOI
An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning
Junfeng Lu,Han Zhang,N U Farrukh Hameed,Jie Zhang,Shiwen Yuan,Tianming Qiu,Dinggang Shen,Dinggang Shen,Jinsong Wu +8 more
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.