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A. Vamvakas et al.
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A. Lasocki et al.
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Wei Zhao et al.
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Javaria Amin et al.
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Brain tumor classification using deep CNN features via transfer learning
S. Deepak et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2019)
Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas
Niha Beig et al.
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Gopal S. Tandel et al.
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Xu Bi et al.
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Mohammadhadi Khorrami et al.
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3D Dilated Multi-fiber Network for Real-Time Brain Tumor Segmentation in MRI
Chen Chen et al.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT III (2019)
Glioma grading using structural magnetic resonance imaging and molecular data
Syed M. S. Reza et al.
JOURNAL OF MEDICAL IMAGING (2019)
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Nathaniel Braman et al.
JAMA NETWORK OPEN (2019)
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Jin Liu et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2018)
Multiparametric Analysis of Permeability and ADC Histogram Metrics for Classification of Pediatric Brain Tumors by Tumor Grade
S. Vajapeyam et al.
AMERICAN JOURNAL OF NEURORADIOLOGY (2018)
Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network
Jin Liu et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2018)
Normalization of ADC does not improve correlation with overall survival in patients with high-grade glioma (HGG)
Lei Qin et al.
JOURNAL OF NEURO-ONCOLOGY (2018)
Multi-center study finds postoperative residual non-enhancing component of glioblastoma as a new determinant of patient outcome
Aikaterini Kotrotsou et al.
JOURNAL OF NEURO-ONCOLOGY (2018)
Glioma Grading on Conventional MR Images: A Deep Learning Study With Transfer Learning
Yang Yang et al.
FRONTIERS IN NEUROSCIENCE (2018)
Classification of the glioma grading using radiomics analysis
Hwan-ho Cho et al.
PEERJ (2018)
Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging
Ken Chang et al.
CLINICAL CANCER RESEARCH (2018)
Applications of Deep Learning to MRI Images: A Survey
Jin Liu et al.
BIG DATA MINING AND ANALYTICS (2018)
Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning
Saima Rathore et al.
JOURNAL OF MEDICAL IMAGING (2018)
Radiomic features from the peritumoral brain parenchyma on treatment-na⟨ve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings
Prateek Prasanna et al.
EUROPEAN RADIOLOGY (2017)
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Zeynettin Akkus et al.
JOURNAL OF DIGITAL IMAGING (2017)
Current Clinical Brain Tumor Imaging
Javier E. Villanueva-Meyer et al.
NEUROSURGERY (2017)
Local binary features for texture classification: Taxonomy and experimental study
Li Liu et al.
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Julien Faget et al.
CELL REPORTS (2017)
Computational Radiomics System to Decode the Radiographic Phenotype
Joost J. M. van Griethuysen et al.
CANCER RESEARCH (2017)
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer
Yan-qi Huang et al.
JOURNAL OF CLINICAL ONCOLOGY (2016)
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2015)
Characterizing the peritumoral brain zone in glioblastoma: a multidisciplinary analysis
Jean-Michel Lemee et al.
JOURNAL OF NEURO-ONCOLOGY (2015)
Intratumoral heterogeneity in glioblastoma: don't forget the peritumoral brain zone
Jean-Michel Lemee et al.
NEURO-ONCOLOGY (2015)
Peritumoral edema on magnetic resonance imaging predicts a poor clinical outcome in malignant glioma
Chen-Xing Wu et al.
ONCOLOGY LETTERS (2015)
Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor
Rajan Jain et al.
RADIOLOGY (2014)
Radiomics: Extracting more information from medical images using advanced feature analysis
Philippe Lambin et al.
EUROPEAN JOURNAL OF CANCER (2012)
The 2007 WHO classification of tumours of the central nervous system
David N. Louis et al.
ACTA NEUROPATHOLOGICA (2007)
Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy
HC Peng et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2005)
An introduction to logistic regression analysis and reporting
CYJ Peng et al.
JOURNAL OF EDUCATIONAL RESEARCH (2002)