L
Lei Jin
Researcher at Fudan University
Publications - 9
Citations - 127
Lei Jin is an academic researcher from Fudan University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 5, co-authored 7 publications receiving 50 citations.
Papers
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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
Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers
Lei Jin,Feng Shi,Chun Qiuping,Hong Chen,Yixin Ma,Shuai Wu,N U Farrukh Hameed,Chunming Mei,Junfeng Lu,Jun Zhang,Abudumijiti Aibaidula,Dinggang Shen,Jinsong Wu +12 more
TL;DR: A new model of the squeeze-and-excitation block DenseNet with weighted cross-entropy (named SD-Net_WCE) is developed for the glioma classification task, which is capable of solving multiple classification tasks and can satisfactorily able to classifyglioma subtypes.
Journal ArticleDOI
Multi-Label Nonlinear Matrix Completion With Transductive Multi-Task Feature Selection for Joint MGMT and IDH1 Status Prediction of Patient With High-Grade Gliomas
Lei Chen,Han Zhang,Junfeng Lu,Kim-Han Thung,Abudumijiti Aibaidula,Luyan Liu,Songcan Chen,Lei Jin,Jinsong Wu,Qian Wang,Liangfu Zhou,Dinggang Shen +11 more
TL;DR: A novel multi-label nonlinear matrix completion (MNMC) model is proposed to jointly predict both MGMT and IDH1 statuses in a multi-task framework and the promise of utilizing brain connectomics for HGG prognosis in a non-invasive manner is shown.
Book ChapterDOI
Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype
Zhenyu Tang,Yuyun Xu,Zhicheng Jiao,Junfeng Lu,Lei Jin,Abudumijiti Aibaidula,Jinsong Wu,Qian Wang,Han Zhang,Dinggang Shen +9 more
TL;DR: A new deep learning based method that can derive genotype related features from pre-operative multimodal MR images of brain tumor patients to guide OS time prediction is proposed and it is concluded that the multi-task learning can effectively improve the accuracy of predicting OS time in personalized prognosis.
Journal ArticleDOI
Unforgettable Ups and Downs of Acupuncture Anesthesia in China.
TL;DR: This historical vignette introduced the development, mechanism research, awake craniotomy, in order to analyze the utility of acupuncture anesthesia, its global impact, the current situation and future of acupunctureesthesia.