Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics
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Lihui Wang | Jian Zhang | Zeyu Deng | Qijian Chen | Yuemin Zhu | Li Wang | Jian Zhang | Li Wang | Yuemin M. Zhu | Zeyu Deng | Qijian Chen | Lihui Wang
[1] Zhiqiang Zhang,et al. Comparison of Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging and Arterial Spin Labeling MR Imaging in Gliomas , 2015, BioMed research international.
[2] Stéphane Mallat,et al. Group Invariant Scattering , 2011, ArXiv.
[3] A. Madabhushi,et al. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings , 2017, European Radiology.
[4] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[5] Stéphane Mallat,et al. Invariant Scattering Convolution Networks , 2012, IEEE transactions on pattern analysis and machine intelligence.
[6] Stéphane Mallat,et al. Generic Deep Networks with Wavelet Scattering , 2013, ICLR.
[7] Sang Joon Park,et al. Glioma: Application of Whole-Tumor Texture Analysis of Diffusion-Weighted Imaging for the Evaluation of Tumor Heterogeneity , 2014, PloS one.
[8] Perinodular Radiomic Features to Assess Nodule Microenvironment: Does It Help to Distinguish Malignant versus Benign Lung Nodules? , 2019, Radiology.
[9] Stephen M. Moore,et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.
[10] Gongping Yang,et al. Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification , 2018, IEEE Journal of Biomedical and Health Informatics.
[11] Massimo Caulo,et al. Data-driven grading of brain gliomas: a multiparametric MR imaging study. , 2014, Radiology.
[12] Jean YH Yang,et al. Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.
[13] Stéphane Mallat,et al. Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[14] Irene Y. H. Gu,et al. Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification , 2019, CAIP.
[15] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[16] K. Yeom,et al. Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches , 2017, American Journal of Neuroradiology.
[17] B. Kowalski,et al. Partial least-squares regression: a tutorial , 1986 .
[18] Yang Yang,et al. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features , 2017, Oncotarget.
[19] Hyunjin Park,et al. Classification of low-grade and high-grade glioma using multi-modal image radiomics features , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[20] Adolf Pfefferbaum,et al. The SRI24 multichannel atlas of normal adult human brain structure , 2009, Human brain mapping.
[21] Altug Akay,et al. Deep Learning: Current and Emerging Applications in Medicine and Technology , 2019, IEEE Journal of Biomedical and Health Informatics.
[22] P. Wesseling,et al. WHO 2016 Classification of gliomas , 2018, Neuropathology and applied neurobiology.
[23] C. Y. Peng,et al. An Introduction to Logistic Regression Analysis and Reporting , 2002 .
[24] Irene Y. H. Gu,et al. 3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).
[25] Johan Trygg,et al. ADC texture--an imaging biomarker for high-grade glioma? , 2014, Medical physics.
[26] Christos Davatzikos,et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme , 2009, Magnetic resonance in medicine.
[27] K. Kono,et al. The role of diffusion-weighted imaging in patients with brain tumors. , 2001, AJNR. American journal of neuroradiology.
[28] E G Stopa,et al. Observer reliability in histological grading of astrocytoma stereotactic biopsies. , 1996, Journal of neurosurgery.
[29] Milan Decuyper,et al. Binary Glioma Grading: Radiomics versus Pre-trained CNN Features , 2018, MICCAI.
[30] N. Paragios,et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.
[31] Koji Yamashita,et al. Differentiation of high-grade and low-grade diffuse gliomas by intravoxel incoherent motion MR imaging. , 2016, Neuro-oncology.
[32] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[33] Qiang Tian,et al. Radiomics strategy for glioma grading using texture features from multiparametric MRI , 2018, Journal of magnetic resonance imaging : JMRI.
[34] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[35] K. Tomczak,et al. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge , 2015, Contemporary oncology.
[36] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[37] Quanshi Zhang,et al. Visual interpretability for deep learning: a survey , 2018, Frontiers of Information Technology & Electronic Engineering.
[38] Gary King,et al. Logistic Regression in Rare Events Data , 2001, Political Analysis.
[39] A. Madabhushi,et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI , 2017, Breast Cancer Research.
[40] Hyunjin Park,et al. Classification of the glioma grading using radiomics analysis , 2018, PeerJ.
[41] Jie Yang,et al. Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[42] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[43] Christos Davatzikos,et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features , 2017, Scientific Data.
[44] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[45] P. Wesseling. Classification of Gliomas , 2013 .
[46] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .