A cost-sensitive Bayesian combiner for reducing false positives in mammographic mass detection

References

[1] Alpaydin E. Introduction to machine learning. Chapter 15. Cambridge, MA: MIT Press 2004: 364–366.Suche in Google Scholar

[2] Alpaydin E. Introduction to machine learning. Chapter 3. Cambridge, MA: MIT Press 2004: 48–55.Suche in Google Scholar

[3] Anitha J, Peter JD, Pandian SIA. A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms. Comput Methods Programs Biomed 2017; 138: 93–104.10.1016/j.cmpb.2016.10.026Suche in Google Scholar PubMed

[4] Breiman L. Bagging predictors. Mach Learning 1996; 24: 123–140.10.1007/BF00058655Suche in Google Scholar

[5] Breiman L. Stacked regression. Mach Learning 1996; 24: 49–64.10.1007/BF00117832Suche in Google Scholar

[6] Breiman L. Random forests. Mach Learning 2001; 45: 5–32.10.1023/A:1010933404324Suche in Google Scholar

[7] Chawla N. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 2002; 16: 321–357.10.1613/jair.953Suche in Google Scholar

[8] Christoyanni I, Dermatas E, Kokkinakis G. Fast detection of masses in computer aided mammography. IEEE Signal Process Mag 2000; 17: 54–64.10.1109/79.814646Suche in Google Scholar

[9] Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans Image Process 2007; 16: 1–16.10.1109/TIP.2007.901238Suche in Google Scholar

[10] Dehungle N, Carneiro G, Bradley AP. Automated mass detection in mammograms using cascaded deep learning and random forests. In: IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA). Adelaide, SA, Australia: IEEE 2015: 1–8.10.1109/DICTA.2015.7371234Suche in Google Scholar

[11] Domingos P. A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining. New York: ACM Press 1999: 155–164.Suche in Google Scholar

[12] Dominguez A, Nandi A. Detection of masses in mammograms via statistically based enhancement, multi-level thresholding, and region selection. Comput Med 2008; 32: 304–315.Suche in Google Scholar

[13] Eltonsy NH, Tourassi GD, Elmaghraby AS. A concentric morphology model for the detection of masses in mammography. IEEE Trans Med Imaging 2007; 26: 880–889.10.1109/TMI.2007.895460Suche in Google Scholar PubMed

[14] Fauci F, Raso G, Magro R, et al. A massive lesion detection algorithm in mammography. Phys Med 2005; 21: 23–30.10.1016/S1120-1797(05)80016-XSuche in Google Scholar PubMed

[15] Fayyad U, Irani K. Multi-interval discretization of continuous valued attributes for classification learning. In: 13th International Joint Conference on Artificial Intelligence (IJCAI). San Mateo, CA 1993: 1022–1027.Suche in Google Scholar

[16] Freund Y, Schapire RE. Experiments with a new boosting algorithm. In: 13th International Conference on Machine Learning. San Francisco 1996: 148–156.Suche in Google Scholar

[17] Gedik N, Atasoy N. A computer aided diagnosis system for breast cancer detection by using a curvelet transform. Turk J Elec Eng Comp Sci 2013; 21: 1002–1014.10.3906/elk-1201-8Suche in Google Scholar

[18] Hall M. Correlation-based feature subset selection for machine learning. PhD thesis, University of Waikato, Hamilton, New Zealand, 1999.Suche in Google Scholar

[19] Hall M, Smith L. Feature selection for machine learning: comparing correlation-based filter approach to the wrapper. In: 12th International Florida Artificial Intelligence Research Society Conference (FLAIRS-99). Orlando, FL, USA 1999.Suche in Google Scholar

[20] Haralick RM, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; 3: 610–621.10.1109/TSMC.1973.4309314Suche in Google Scholar

[21] Hussain M, Khan N. Automatic mass detection in mammograms using multiscale spatial weber local descriptors. In: 19th International Conference on Systems, Signals and Image Processing (IWSSIP). Vienna, Austria: IEEE 2012: 288–291.Suche in Google Scholar

[22] Kobatake H, Murakami M, Takeo H, Nawano S. Computerized detection of malignant tumors on digital mammograms. IEEE Trans Med Imag 1999; 18: 369–378.10.1109/42.774164Suche in Google Scholar PubMed

[23] Kom G, Tiedeu A, Kom M. Automated detection of masses in mammograms by local adaptive thresholding. Comput Biol Med 2007; 37: 37–48.10.1016/j.compbiomed.2005.12.004Suche in Google Scholar PubMed

[24] Kooi T, Litjens G, van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 2017; 35: 303–312.10.1016/j.media.2016.07.007Suche in Google Scholar PubMed

[25] Kozegar E, Soryani M, Domingues I. A new local adaptive mass detection algorithm in mammograms. In: International Conference on Bio-inspired Systems and Signal Processing. Barcelona, Spain 2013: 133–137.Suche in Google Scholar

[26] Kozegar E, Soryani M, Minaei B, Domingues I. Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther 2013; 9: 592–600.10.4103/0973-1482.126453Suche in Google Scholar PubMed

[27] Kuncheva LI. Combining pattern classifiers: methods and algorithms. Chapter 3. Hoboken, NJ: John Wiley & Sons Inc 2004: 101–109.10.1002/0471660264.ch3Suche in Google Scholar

[28] Kuncheva LI. Combining pattern classifiers: methods and algorithms. Chapter 2. Hoboken, NJ: John Wiley & Sons Inc 2004: 88–95.10.1002/0471660264Suche in Google Scholar

[29] Li N, Zhou HJ, Guo Q, Yang Y. A cost sensitive cascaded method for automatic mass detection. In: International Conference on Systems, Man and Cybernetics. Singapore: IEEE 2008: 3454–3458.Suche in Google Scholar

[30] Li Y, Chen H, Cheng L, Cao L. A bilateral analysis scheme for false positive reduction in mammogram mass detection. Comput Biol Med 2015; 57: 84–95.10.1016/j.compbiomed.2014.12.007Suche in Google Scholar PubMed

[31] Malagelada AO. Automatic mass segmentation in mammographic images. PhD thesis, Department of Electronics, Computer Science and Automatic Control, University of De Girona, Girona, Spain, 2007Suche in Google Scholar

[32] Masotti M, Campanini R. Texture classification using invariant ranklet features. Pattern Recognit Lett 2008; 29: 1980–1986.10.1016/j.patrec.2008.06.017Suche in Google Scholar

[33] Melville P, Mooney RJ. Constructing diverse classifier ensembles using artificial training examples. In: 13th International Joint Conference on Artificial Intelligence, Acapulco, Mexico 2003: 505–510.Suche in Google Scholar

[34] Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. Towards a full field digital mammographic database. Acad Radiol 2012; 19: 236–248.10.1016/j.acra.2011.09.014Suche in Google Scholar PubMed

[35] Nguyen VD, Nguyen DT, Nguyen TD, Thi N, Tran DH. A program for locating possible breast masses on mammograms. In: Proceeding of the 3rd International Conference on the Development of BME. Vietnam: Springer 2010: 11–14.10.1007/978-3-642-12020-6_27Suche in Google Scholar

[36] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002; 24: 971–978.10.1109/TPAMI.2002.1017623Suche in Google Scholar

[37] Oliver A, Freixenet J, Marti J, et al. A review of automatic mass detection and segmentation in mammographic images. Med Image Anal 2010; 14: 87–110.10.1016/j.media.2009.12.005Suche in Google Scholar PubMed

[38] Oliviera MD, Braz J, Cardoso P, Gattass M. Detection of masses in digital mammograms using K-means and support vector machine. Electron Lett Comput Vis Image Anal 2009; 8: 39–50.10.5565/rev/elcvia.216Suche in Google Scholar

[39] Petrick N, Chan H, Sahiner B, Wei D. An adaptive density weighted contrast enhancement filter for mammographic breast mass detection. IEEE Trans Med Imaging 1996; 15: 59–67.10.1109/42.481441Suche in Google Scholar PubMed

[40] Polakowski WE, Cournoyer D, Rogers SK, et al. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussian and derivative-based feature saliency. IEEE Trans Med Imaging 1997; 16: 811–819.10.1109/42.650877Suche in Google Scholar PubMed

[41] Rodriguez JJ, Kuncheva LI, Alonso CJ. Random forests. IEEE Trans Pattern Anal Mach Intell 2006; 28: 1619–1630.10.1109/TPAMI.2006.211Suche in Google Scholar PubMed

[42] Smyth, Wolpert D. An evaluation of linearity combining density estimators via stacking. Mach Learn 1999; 36: 59–83.10.1023/A:1007511322260Suche in Google Scholar

[43] Soh L, Tsatsoulis C. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 1999; 73: 780–795.10.1109/36.752194Suche in Google Scholar

[44] Sree SV, Ng EYK, Acharya RU, Faust O. Breast imaging: a survey. World J Clin Oncol 2011; 2: 171–178.10.5306/wjco.v2.i4.171Suche in Google Scholar PubMed PubMed Central

[45] Suckling J. The mammographic image analysis society digital mammogram database. In: Exerpta Medica International Congress Series, vol. 1069. 1994: 375–378.Suche in Google Scholar

[46] Tai S, Chen Z, Tsai W. An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Inform 2013; 18: 618–627.Suche in Google Scholar

[47] Varela C, Tahoces P, Mendez A, Souto M, Vidal J. Computerized detection of breast masses in digitized mammograms. Comput Biol Med 2007; 37: 214–226.10.1016/j.compbiomed.2005.12.006Suche in Google Scholar PubMed

[48] Vikhe P, Thool V. Mass detection in mammographic images using wavelet processing and adaptive threshold technique. J Med Syst 2016; 40: 1–16.10.1007/s10916-016-0435-3Suche in Google Scholar PubMed

[49] Yang S, Wang C, Chung Y. A computer aided system for mass detection and classification in digitized mammograms. Biomed Eng App Basis Commun 2005; 17: 215–228.10.4015/S1016237205000330Suche in Google Scholar