Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images

Amira S. Ashour1, Yanhui Guo2, Ahmed Refaat Hawas1, Guan Xu3
1Department of Electronics and Electrical Communication Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt
2Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA
3Department of Radiology, University of Michigan Medical School, Ann Arbor, USA

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Chaves NJ, Gibney KB, Leder K, O’brien DP, Marshall C, Biggs BA. Screening practices for infectious diseases among Burmese refugees in Australia. Emerging Infectious Dis. 2009;15(11):1769.

Xia JL, Dai C, Michalopoulos GK, Liu Y. Hepatocyte growth factor attenuates liver fibrosis induced by bile duct ligation. The American J Pathol. 2006;168(5):1500–12.

Sun W, Chang S, Tai DC, Tan N, Xiao G, Tang H, Yu H. Nonlinear optical microscopy: use of second harmonic generation and two-photon microscopy for automated quantitative liver fibrosis studies. J Biomed Opt. 2008;13(6):064010.

Mabey D, Peeling RW, Ustianowski A, Perkins MD. Tropical infectious diseases: diagnostics for the developing world. Nat Rev Microbiol. 2004;2(3):231.

Mahmoud-Ghoneim D. Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions. Theor Biol Med Modell. 2011;8(1):25.

Ali S, Smith KA. On learning algorithm selection for classification. Appl Soft Comput. 2006;6(2):119–38.

Kuncheva LI. Combining pattern classifiers: methods and algorithms. New York: Wiley; 2004.

Woods K, Kegelmeyer WP, Bowyer K. Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell. 1997;19(4):405–10.

Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst Mag. 2006;6(3):21–45.

Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst Mag. 2006;6(3):21–45.

Zhang C, Ma Y, editors. Ensemble machine learning: methods and applications. New York: Springer Science & Business Media; 2012.

Rahman A, Verma B. Cluster-based ensemble of classifiers. Exp Syst. 2013;30(3):270–82.

Tao D, Tang X, Li X, Wu X. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell. 2006;28(7):1088–99.

García-Pedrajas N, Ortiz-Boyer D. Boosting random subspace method. Neural Netw. 2008;21(9):1344–62.

Kotsiantis S. Combining bagging, boosting, rotation forest and random subspace methods. Artif Intell Rev. 2011;35(3):223–40.

Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998;20(8):832–44.

Kuncheva LI, Rodríguez JJ, Plumpton CO, Linden DE, Johnston SJ. Random subspace ensembles for fMRI classification. IEEE Trans Med Imaging. 2010;29(2):531–42.

Panov P, Džeroski S. Combining bagging and random subspaces to create better ensembles. In: International Symposium on Intelligent Data Analysis. Springer, Berlin, Heidelberg; 2007. pp. 118-129.

Skurichina M, Duin RP. Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl. 2002;5(2):121–35.

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M,… & Sánchez CI. A survey on deep learning in medical image analysis. Medical Image Anal. 2017;42:60–88.

Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.