An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries
暂无分享,去创建一个
Jia Wu | Jun Zhang | Zhigang Chen | Kanghuai Liu | Kanghuai Liu | Jia Wu | Jun Zhang | Zhigang Chen
[1] Luiz Eduardo Soares de Oliveira,et al. Breast cancer histopathological image classification using Convolutional Neural Networks , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[2] N. Hirmas,et al. [68Ga]PSMA PET/CT Improves Initial Staging and Management Plan of Patients with High-Risk Prostate Cancer , 2018, Molecular Imaging and Biology.
[3] Michael Berks,et al. Prediction of reader estimates of mammographic density using convolutional neural networks , 2019, Journal of medical imaging.
[4] F. Bray,et al. National economic and development indicators and international variation in prostate cancer incidence and mortality: an ecological analysis , 2017, World Journal of Urology.
[5] S. Edge,et al. Breast Cancer—Major changes in the American Joint Committee on Cancer eighth edition cancer staging manual , 2017, CA: a cancer journal for clinicians.
[6] Zhigang Chen,et al. Decision based on big data research for non-small cell lung cancer in medical artificial system in developing country , 2018, Comput. Methods Programs Biomed..
[7] J. Ferlay,et al. Global cancer incidence in older adults, 2012 and 2035: A population‐based study , 2018, International journal of cancer.
[8] Jia Wu,et al. Diagnosis and Data Probability Decision Based on Non-Small Cell Lung Cancer in Medical System , 2019, IEEE Access.
[9] S. Geeitha,et al. Incorporating EBO-HSIC with SVM for Gene Selection Associated with Cervical Cancer Classification , 2018, Journal of Medical Systems.
[10] E. Schaeffer,et al. Re: Active surveillance for prostate cancer compared with immediate treatment: an economic analysis. , 2013, The Journal of urology.
[11] A. Jemal,et al. Annual Report to the Nation on the status of cancer, 1975‐2010, featuring prevalence of comorbidity and impact on survival among persons with lung, colorectal, breast, or prostate cancer , 2014, Cancer.
[12] M. Bock,et al. Comparison of 68Ga-HBED-CC PSMA-PET/CT and multiparametric MRI for gross tumour volume detection in patients with primary prostate cancer based on slice by slice comparison with histopathology , 2017, Theranostics.
[13] E. Uzunhisarcıklı,et al. A novel classifier model for mass classification using BI-RADS category in ultrasound images based on Type-2 fuzzy inference system , 2018, Sādhanā.
[14] Alan D. Lopez,et al. Counting the dead and what they died from: an assessment of the global status of cause of death data. , 2005, Bulletin of the World Health Organization.
[15] T. Coroller,et al. Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging , 2019, Clinical Cancer Research.
[16] Murray H. Loew,et al. Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks , 2018, Other Conferences.
[17] C. Liang,et al. The establishment of immune infiltration based novel recurrence predicting nomogram in prostate cancer , 2019, Cancer medicine.
[18] A. Kural,et al. The accuracy of 68Ga-PSMA PET/CT in primary lymph node staging in high-risk prostate cancer , 2017, European Journal of Nuclear Medicine and Molecular Imaging.
[19] Asad Malik,et al. Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings , 2019, IEEE Access.
[20] C. Pui,et al. Saving the children--improving childhood cancer treatment in developing countries. , 2005, The New England journal of medicine.
[21] Jalaluddin Khan,et al. Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data , 2020, Sensors.
[22] M. Schonberg,et al. Cancer Screening in the Elderly: A Review of Breast, Colorectal, Lung, and Prostate Cancer Screening , 2017, Cancer journal.
[23] A. Bordoni,et al. Trends in prostate cancer incidence between 1996 and 2013 in two Swiss regions by age, grade, and T-stage , 2018, Cancer Causes & Control.
[24] O. Kim,et al. miRNA polymorphisms (miR‑146a, miR‑149, miR‑196a2 and miR‑499) are associated with the risk of coronary artery disease. , 2016, Molecular medicine reports.
[25] Tapabrata Maiti,et al. Bayesian neural networks for bivariate binary data: an application to prostate cancer study , 2005, Statistics in medicine.
[26] D. Pauler,et al. Predicting time to prostate cancer recurrence based on joint models for non‐linear longitudinal biomarkers and event time outcomes , 2002, Statistics in medicine.
[27] Jian-Ping Li,et al. A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms , 2018, Mob. Inf. Syst..
[28] Yingchun Zhang,et al. A Fuzzy Reasoning Model for Cervical Intraepithelial Neoplasia Classification Using Temporal Grayscale Change and Textures of Cervical Images During Acetic Acid Tests , 2019, IEEE Access.
[29] D. T. Arp,et al. Prospective comparison of 68Ga-PSMA PET/CT, 18F-sodium fluoride PET/CT and diffusion weighted-MRI at for the detection of bone metastases in biochemically recurrent prostate cancer , 2018, European Journal of Nuclear Medicine and Molecular Imaging.
[30] U. Rajendra Acharya,et al. A novel machine learning approach for early detection of hepatocellular carcinoma patients , 2019, Cognitive Systems Research.
[31] Cécile Proust-Lima,et al. A joint model for multiple dynamic processes and clinical endpoints: Application to Alzheimer's disease , 2018, Statistics in medicine.
[32] P. Vineis,et al. Global cancer patterns: causes and prevention , 2014, The Lancet.
[33] Yi-long Wu,et al. Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network. , 2019, The oncologist.
[34] Zhigang Chen,et al. Big Medical Data Decision-Making Intelligent System Exploiting Fuzzy Inference Logic for Prostate Cancer in Developing Countries , 2019, IEEE Access.
[35] H. Heinzer,et al. Initial Experience of (68)Ga-PSMA PET/CT Imaging in High-risk Prostate Cancer Patients Prior to Radical Prostatectomy. , 2016, European urology.
[36] Hua Li,et al. Benign and malignant classification of mammogram images based on deep learning , 2019, Biomed. Signal Process. Control..
[37] F. Cavalli. Cancer in the developing world: can we avoid the disaster? , 2006, Nature Clinical Practice Oncology.