Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images
Tóm tắt
Từ khóa
Tài liệu tham khảo
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.
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.
Zhang C, Ma Y, editors. Ensemble machine learning: methods and applications. New York: Springer Science & Business Media; 2012.
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.
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.