Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
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Tomohiro Kuroda | Ryosuke Kojima | Mizuho Nishio | Kaori Togashi | Osamu Sugiyama | Masahiro Yakami | Mitsuo Nishizawa | T. Kuroda | K. Togashi | M. Nishio | Osamu Sugiyama | Ryosuke Kojima | M. Nishizawa | M. Yakami
[1] Yeni Herdiyeni,et al. Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis , 2012 .
[2] Yoshua Bengio,et al. Algorithms for Hyper-Parameter Optimization , 2011, NIPS.
[3] Hui Chen,et al. Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation. , 2010, Academic radiology.
[4] Abbas Z. Kouzani,et al. Automated detection of lung nodules in computed tomography images: a review , 2010, Machine Vision and Applications.
[5] Rebecca L. Siegel Mph,et al. Cancer statistics, 2016 , 2016 .
[6] D. Aberle,et al. Computed tomography screening for lung cancer: has it finally arrived? Implications of the national lung screening trial. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[7] Ayman El-Baz,et al. Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies , 2013, Int. J. Biomed. Imaging.
[8] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[9] Karen Drukker,et al. LUNGx Challenge for computerized lung nodule classification , 2016, Journal of medical imaging.
[10] D. Capistrano,et al. Reflections and lessons learned , 2020, Planners in Politics.
[11] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[12] Olivier Gevaert,et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.
[13] Athanasios V. Vasilakos,et al. Neural networks for computer-aided diagnosis in medicine: A review , 2016, Neurocomputing.
[14] Yang Liu,et al. Symptom severity classification with gradient tree boosting. , 2017, Journal of biomedical informatics.
[15] Guillaume Chassagnon,et al. Computer-aided diagnosis (CAD) of subsolid nodules: Evaluation of a commercial CAD system. , 2016, European journal of radiology.
[16] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[17] Hidetaka Arimura,et al. Image-Based Computer-Assisted Radiation Therapy , 2017 .
[18] Xiao Chen,et al. Bayesian optimization algorithm-based methods searching for risk/protective factors , 2013, Chinese Science Bulletin.
[19] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[20] Wolfram Burgard,et al. Automatic bone parameter estimation for skeleton tracking in optical motion capture , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[21] Matti Pietikäinen,et al. A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..
[22] Karen Drukker,et al. LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. , 2015, Journal of medical imaging.
[23] Bin Chen,et al. A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists , 2017, International Journal of Computer Assisted Radiology and Surgery.
[24] Wei Li,et al. A multi-kernel based framework for heterogeneous feature selection and over-sampling for computer-aided detection of pulmonary nodules , 2017, Pattern Recognit..
[25] E. Kazerooni,et al. Computer-aided diagnosis of lung nodules on CT scans: ROC study of its effect on radiologists' performance. , 2010, Academic radiology.
[26] A. Jemal,et al. Cancer statistics, 2016 , 2016, CA: a cancer journal for clinicians.
[27] Kenji Suzuki,et al. Computer-Aided Detection of Lung Cancer , 2017 .
[28] M. L. R. D. Christenson,et al. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .
[29] D. Xu,et al. Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers. , 2016, Radiology.
[30] C. Gatsonis,et al. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .
[31] Matti Pietikäinen,et al. Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Robert C. Wolpert,et al. A Review of the , 1985 .
[33] Mizuho Nishio,et al. Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity. , 2017, Academic radiology.
[34] Christoforos Anagnostopoulos,et al. Stopping criteria for boosting automatic experimental design using real-time fMRI with Bayesian optimization , 2015 .
[35] Yeni Herdiyeni,et al. Comparison of 2 D and 3 D Local Binary Pattern in Lung Cancer Diagnosis , 2012 .
[36] Christoforos Anagnostopoulos,et al. Towards tailoring non-invasive brain stimulation using real-time fMRI and Bayesian optimization , 2016, 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).
[37] Matti Pietikäinen,et al. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[38] Kenji Suzuki. A review of computer-aided diagnosis in thoracic and colonic imaging. , 2012, Quantitative imaging in medicine and surgery.
[39] Hela Mahersia,et al. Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques , 2015 .
[40] Stephen M. Moore,et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.
[41] N. Dubrawsky. Cancer statistics , 1989, CA: a cancer journal for clinicians.
[42] James C. Gee,et al. Pulmonary nodule detection in CT images with quantized convergence index filter , 2006, Medical Image Anal..
[43] Leslie N. Smith,et al. Best Practices for Applying Deep Learning to Novel Applications , 2017, ArXiv.
[44] Bram van Ginneken,et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning , 2016, Scientific Reports.
[45] Marcela Perrone-Bertolotti,et al. Machine learning–XGBoost analysis of language networks to classify patients with epilepsy , 2017, Brain Informatics.
[46] Anselmo Cardoso de Paiva,et al. Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM , 2017, Journal of Digital Imaging.
[47] Kunio Doi,et al. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..