A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification
暂无分享,去创建一个
Marcone J. F. Souza | Claudia M. Carneiro | Fátima N. S. de Medeiros | Andrea G. C. Bianchi | F. N. S. de Medeiros | Daniela M. Ushizima | Mariana T. Rezende | Débora N. Diniz | D. Ushizima | A. G. Bianchi | C. Carneiro | M. Souza | D. N. Diniz | M. T. Rezende
[1] Marcone J. F. Souza,et al. An Iterated Local Search-Based Algorithm to Support Cell Nuclei Detection in Pap Smears Test , 2019, ICEIS.
[2] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[3] M. Lepe,et al. Atypical Glandular Cells: Interobserver Variability according to Clinical Management , 2018, Acta Cytologica.
[4] Sameer Antani,et al. Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification , 2019, MICCAI.
[5] Ghassan Hamarneh,et al. Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells , 2017, IEEE Journal of Biomedical and Health Informatics.
[6] M. Teague. Image analysis via the general theory of moments , 1980 .
[7] R. Nayar,et al. Bethesda 2014: improving on a paradigm shift , 2015, Cytopathology : official journal of the British Society for Clinical Cytology.
[8] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[9] D. Davey,et al. Quality Assurance and Risk Reduction Guidelines , 2000, Acta Cytologica.
[10] Ling Zhang,et al. Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks , 2018, IEEE Access.
[11] Trevor Hastie,et al. Multi-class AdaBoost ∗ , 2009 .
[12] A. Jemal,et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.
[13] A. Jemal,et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries , 2021, CA: a cancer journal for clinicians.
[14] Damminda Alahakoon,et al. Minority report in fraud detection: classification of skewed data , 2004, SKDD.
[15] Siegfried Kropf,et al. A Ridge Classification Method for High-dimensional Observations , 2005, GfKl.
[16] Cecilia Di Ruberto,et al. Histological Image Analysis by Invariant Descriptors , 2017, ICIAP.
[17] Na Dong,et al. Inception v3 based cervical cell classification combined with artificially extracted features , 2020, Appl. Soft Comput..
[18] Geoffrey E. Hinton. Connectionist Learning Procedures , 1989, Artif. Intell..
[19] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[20] Sirlei Siani Morais,et al. Fatores associados a resultados falso-negativos de exames citopatológicos do colo uterino , 2006 .
[21] A Singer,et al. Report on consensus conference on cervical cancer screening and management , 2000, International journal of cancer.
[22] Nitesh V. Chawla,et al. Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.
[23] False-Negative Rate of Papanicolaou Testing: A National Survey from the Thai Society of Cytology , 2017, Acta Cytologica.
[24] Emmanuelle Gouillart,et al. scikit-image: image processing in Python , 2014, PeerJ.
[25] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.
[26] N. Altman. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .
[27] Eddy Sánchez-Delacruz,et al. Classification of Cervical Cancer Using Assembled Algorithms in Microscopic Images of Papanicolaou , 2017, Res. Comput. Sci..
[28] R. Tibshirani,et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[29] W. Kruskal,et al. Use of Ranks in One-Criterion Variance Analysis , 1952 .
[30] Leslie Pérez Cáceres,et al. The irace package: Iterated racing for automatic algorithm configuration , 2016 .
[31] Luis Pedro Coelho,et al. Mahotas: Open source software for scriptable computer vision , 2012, ArXiv.
[32] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[33] Nicholas A. Hamilton,et al. Fast automated cell phenotype image classification , 2007, BMC Bioinformatics.
[34] S. Shapiro,et al. An Analysis of Variance Test for Normality (Complete Samples) , 1965 .
[35] Matti Pietikäinen,et al. Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2000, ECCV.
[36] Lipi B. Mahanta,et al. A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network. , 2020, Tissue & cell.
[37] Fernando Nogueira,et al. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning , 2016, J. Mach. Learn. Res..
[38] Francisco Herrera,et al. Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling , 2011, Soft Comput..
[39] E. Lazcano-Ponce,et al. Assessment of the Validity and Reproducibility of the Pap Smear in Mexico: Necessity of a Paradigm Shift. , 2015, Archives of medical research.
[40] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[41] J. A. Ware,et al. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images , 2018, Comput. Methods Programs Biomed..
[42] James Geller,et al. Data Mining: Practical Machine Learning Tools and Techniques - Book Review , 2002, SIGMOD Rec..
[43] S. Naryshkin. The false-negative fraction for Papanicolaou smears: how often are "abnormal" smears not detected by a "standard" screening cytologist? , 1997, Archives of pathology & laboratory medicine.
[44] Meenakshi Singh,et al. A Study on Cervical Cancer Screening Using Pap Smear Test and Clinical Correlation , 2018, Asia-Pacific journal of oncology nursing.
[45] Flávio H. D. Araújo,et al. Searching for cell signatures in multidimensional feature spaces , 2021, International Journal of Biomedical Engineering and Technology.
[46] J. Goellner,et al. False-negative results in cervical cytologic studies. , 1985, Acta cytologica.
[47] David Mease,et al. Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers , 2015, J. Mach. Learn. Res..
[48] Antoine Pirovano,et al. Regression Constraint for an Explainable Cervical Cancer Classifier , 2019, ArXiv.
[49] M. Shamim Hossain,et al. Cervical cancer classification using convolutional neural networks and extreme learning machines , 2020, Future Gener. Comput. Syst..
[50] Malay Kumar Kundu,et al. Automated classification of Pap smear images to detect cervical dysplasia , 2017, Comput. Methods Programs Biomed..
[51] M. Boon,et al. Characteristics of false-negative smears tested in the normal screening situation. , 1992, Acta cytologica.
[52] L. C. B. Cury,et al. Avaliação crítica das nomenclaturas diagnósticas dos exames citopatológicos cervicais utilizadas no Sistema Único de Saúde (SUS) , 2011 .
[53] Katsumi Inoue,et al. Relational Reinforcement Learning for Planning with Exogenous Effects , 2017 .
[54] Ahmed Ghoneim,et al. Machine learning for assisting cervical cancer diagnosis: An ensemble approach , 2020, Future Gener. Comput. Syst..