Breast Density Analysis on Mammograms: Application of Machine Learning with Textural Features
暂无分享,去创建一个
M. Sansone | R. Grassi | C. Ricciardi | G. Gatta | M. Belfiore | Francesco Amato | A. M. Ponsiglione | Francesca Angelone | F. Angelone
[1] M. Romano,et al. Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals , 2021, Bioengineering.
[2] Carlo Ricciardi,et al. A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset , 2021, J. Imaging.
[3] Maria Romano,et al. Optimization of an artificial neural network to study accelerations of foetal heart rhythm , 2021, 2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI).
[4] Maria Romano,et al. A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals , 2021, Sensors.
[5] E. Emiliani,et al. A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case–Control YAU Endourology Study from Nine European Centres , 2021, Journal of clinical medicine.
[6] V. Provitera,et al. Positive impact of short-term gait rehabilitation in Parkinson patients: a combined approach based on statistics and machine learning. , 2021, Mathematical biosciences and engineering : MBE.
[7] Alfonso Maria Ponsiglione,et al. Application of DMAIC Cycle and Modeling as Tools for Health Technology Assessment in a University Hospital , 2021, Journal of healthcare engineering.
[8] Alfonso Maria Ponsiglione,et al. Comparison of machine learning algorithms to predict length of hospital stay in patients undergoing heart bypass surgery , 2021, BECB.
[9] Alfonso Maria Ponsiglione,et al. A comparison of different regression and classification methods for predicting the length of hospital stay after cesarean sections , 2021, ICMHI.
[10] M. Cesarelli,et al. Machine learning to predict mortality after rehabilitation among patients with severe stroke , 2020, Scientific Reports.
[11] S. Kwon,et al. Reliability and Clinical Utility of Machine Learning to Predict Stroke Prognosis: Comparison with Logistic Regression , 2020, Journal of stroke.
[12] H. Alkadhi,et al. Radiomics in medical imaging—“how-to” guide and critical reflection , 2020, Insights into Imaging.
[13] P. Gargiulo,et al. Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images , 2020, European journal of translational myology.
[14] R. Steenbakkers,et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. , 2020, Radiology.
[15] M. Cesarelli,et al. Linear discriminant analysis and principal component analysis to predict coronary artery disease , 2020, Health Informatics J..
[16] Reinhilde Jacobs,et al. Radiomics and Machine Learning in Oral Healthcare , 2020, Proteomics. Clinical applications.
[17] Yuntian Chen,et al. Study Progress of Radiomics With Machine Learning for Precision Medicine in Bladder Cancer Management , 2019, Front. Oncol..
[18] Improta Giovanni,et al. Distinguishing Functional from Non-functional Pituitary Macroadenomas with a Machine Learning Analysis , 2019, IFMBE Proceedings.
[19] Antonella Petrillo,et al. Mammographic density: Comparison of visual assessment with fully automatic calculation on a multivendor dataset , 2018, European Radiology.
[20] Stefan Leger,et al. Image biomarker standardisation initiative version 1 . 4 , 2016, 1612.07003.
[21] Aimilia Gastounioti,et al. Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations. , 2016, Medical physics.
[22] E. Conant,et al. Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment , 2016, Breast Cancer Research.
[23] Yuanjie Zheng,et al. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment. , 2015, Medical physics.
[24] D. Easton,et al. Breast cancer screening: time to target women at risk , 2013, British Journal of Cancer.
[25] I. Buchan,et al. Prevention of breast cancer in the context of a national breast screening programme , 2012, Journal of internal medicine.
[26] N. Boyd,et al. Mammographic density and breast cancer risk: current understanding and future prospects , 2011, Breast Cancer Research.
[27] Max Kuhn,et al. Building Predictive Models in R Using the caret Package , 2008 .
[28] Karla Kerlikowske,et al. The mammogram that cried Wolfe. , 2007, The New England journal of medicine.
[29] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[30] V. McCormack,et al. Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis , 2006, Cancer Epidemiology Biomarkers & Prevention.
[31] D. Miglioretti,et al. Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography , 2003, Annals of Internal Medicine.
[32] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[33] M. Cesarelli,et al. Resolution Resampling of Ultrasound Images in Placenta Previa Patients: Influence on Radiomics Data Reliability and Usefulness for Machine Learning , 2020 .
[34] Riaz Ahmed Ahmed Khan,et al. ROCit- An R Package for Performance Assessment of Binary Classifier with Visualization , 2019 .
[35] Max Kuhn,et al. The caret Package , 2007 .