Game theoretic interpretability for learning based preoperative gliomas grading
暂无分享,去创建一个
Zheng Xiao | Kenli Li | Shengli Li | Laifa Ma | Jianlin Li | Xiaoping Yi | KenLi Li | Shengli Li | Xiaoping Yi | Zheng Xiao | Laifa Ma | Jianlin Li | X. Yi
[1] Bettina Kulle,et al. Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. , 2011, European journal of radiology.
[2] Salvatore Cuomo,et al. Decision Making in IoT Environment through Unsupervised Learning , 2020, IEEE Intelligent Systems.
[3] Z L Gokaslan,et al. Limitations of stereotactic biopsy in the initial management of gliomas. , 2001, Neuro-oncology.
[4] P. Lambin,et al. Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology , 2016, Front. Oncol..
[5] Matthew J. Oborski,et al. Cancer Management and Research Dovepress Malignant Gliomas: Current Perspectives in Diagnosis, Treatment, and Early Response Assessment Using Advanced Quantitative Imaging Methods , 2022 .
[6] Olivier Gevaert,et al. Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma , 2015, Journal of medical imaging.
[7] Khan M. Iftekharuddin,et al. Glioma grading using cell nuclei morphologic features in digital pathology images , 2016, SPIE Medical Imaging.
[8] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[9] Po-Whei Huang,et al. Automatic Classification for Pathological Prostate Images Based on Fractal Analysis , 2009, IEEE Transactions on Medical Imaging.
[10] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[11] K. Lovblad,et al. State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications , 2015, Neuroradiology.
[12] E G Stopa,et al. Observer reliability in histological grading of astrocytoma stereotactic biopsies. , 1996, Journal of neurosurgery.
[13] P. Wesseling,et al. WHO 2016 Classification of gliomas , 2018, Neuropathology and applied neurobiology.
[14] Scott M. Lundberg,et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery , 2018, Nature Biomedical Engineering.
[15] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[16] Salvatore Cuomo,et al. Exploring Unsupervised Learning Techniques for the Internet of Things , 2020, IEEE Transactions on Industrial Informatics.
[17] Insuk Sohn,et al. Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach , 2015, Medicine.
[18] A. Friedman,et al. Influence of the type of surgery on the histologic diagnosis in patients with anaplastic gliomas , 1991, Neurology.
[19] Johan Trygg,et al. ADC texture--an imaging biomarker for high-grade glioma? , 2014, Medical physics.
[20] J. Barnholtz-Sloan,et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. , 2012, Neuro-oncology.
[21] I. Tsougos,et al. The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: a review and future perspectives , 2014, Cancer Imaging.
[22] Steven N. Kalkanis,et al. The role of imaging in the management of adults with diffuse low grade glioma , 2015, Journal of Neuro-Oncology.
[23] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[24] Namkug Kim,et al. Deep into the Brain: Artificial Intelligence in Stroke Imaging , 2017, Journal of stroke.
[25] T. Niu,et al. Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI , 2016, Clinical Cancer Research.
[26] Qiang Tian,et al. Radiomics strategy for glioma grading using texture features from multiparametric MRI , 2018, Journal of magnetic resonance imaging : JMRI.
[27] Yanqi Huang,et al. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[28] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[29] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[30] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[31] 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.
[32] Alessandro Olivi,et al. Independent predictors of morbidity after image-guided stereotactic brain biopsy: a risk assessment of 270 cases. , 2005, Journal of neurosurgery.
[33] L D Cromwell,et al. Filtering noise from images with wavelet transforms , 1991, Magnetic resonance in medicine.
[34] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[35] Michal Strzelecki,et al. A software tool for automatic classification and segmentation of 2D/3D medical images , 2013 .
[36] Jie Tian,et al. Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach , 2013, Pattern Recognit..
[37] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[38] Bo Liu,et al. Grading glioma by radiomics with feature selection based on mutual information , 2018, Journal of Ambient Intelligence and Humanized Computing.
[39] Jie Tian,et al. Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging. , 2016, Radiology.
[40] 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).
[41] Chung-Ming Lo,et al. Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI , 2017, PloS one.
[42] Andriy Fedorov,et al. Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.
[43] Hui Kong,et al. A Generalized Laplacian of Gaussian Filter for Blob Detection and Its Applications , 2013, IEEE Transactions on Cybernetics.
[44] Balaji Ganeshan,et al. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. , 2016, European journal of radiology.
[45] Witham,et al. Comprehensive assessment of hemorrhage risks and outcomes after stereotactic brain biopsy. , 2001, Journal of neurosurgery.
[46] Michal Strzelecki,et al. MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..
[47] J. Hanley,et al. The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.
[48] Daniel L. Rubin,et al. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks , 2015, AMIA.
[49] Maurice M. Smith,et al. Stereotactic biopsy in the diagnosis of brain masses: comparison of results of biopsy and resected surgical specimen. , 1989, Neurosurgery.