[1] American Cancer Society. Cancer Facts & Figures 2015[R]. Atlanta:American Cancer Society, 2015:4-6. [2] ROBBINS P, PINDER S, de KLERK N, et al. Histological grading of breast carcinomas:a study of interobserver agreement[J]. Human pathology, 1995, 26(8):873-879. [3] DALTON L W, PINDER S E, ELSTON C E, et al. Histologic grading of breast cancer:linkage of patient outcome with level of pathologist agreement[J]. Modern Pathology, 2000, 13(7):730-735. [4] XIANG L, XU J. Nuclei detection of breast histopathology based on HOG feature and sliding window[J]. Journal of Shandong University:Engineering Science, 2015, 45(1):37-44. (项磊,徐军.基于HOG特征和滑动窗口的乳腺病理图像细胞检测[J].山东大学学报:工学版,2015,45(1):37-44.) [5] MAY M. A better lens on disease[J]. Scientific American, 2010, 302(5):74-77. [6] BOURZAC K. Software:the computer will see you now[J]. Nature, 2013, 502(7473):S92-S94. [7] CHEN J, QU A, WANG L, et al. New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images[J]. Scientific Reports, 2015(5):10690. [8] WANG L, QU A, YUAN J, et al. Relationship between mathematical parameters of breast invasive ductal carcinoma nests and prognosis of patients based on tumor image processing[J]. Acta Biophysica Sinica, 2013, 29(5):360-369. (王林伟,屈爱平,袁静萍,等.图像处理与分析技术研究乳腺浸润性导管癌癌巢数理参数与患者预后的关系[J].生物物理学报,2013,29(5):360-369.) [9] PETUSHI S, GARCIA F U, HABER M M, et al. Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer[J]. BMC Medical Imaging, 2006, 6(1):14. [10] HALL B H, IANOSI-IRIMIE M, JAVIDIAN P, et al. Computer-assisted assessment of the human epidermal growth factor receptor 2 immunohistochemical assay in imaged histologic sections using a membrane isolation algorithm and quantitative analysis of positive controls[J]. BMC Medical Imaging, 2008, 8(1):11. [11] BASAVANHALLY A N, GANESAN S, AGNER S, et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+breast cancer histopathology[J]. IEEE Transactions on Biomedical Engineering, 2010, 57(3):642-653. [12] Di CATALDO S, FICARRA E, ACQUAVIVA A, et al. Automated segmentation of tissue images for computerized IHC analysis[J]. Computer Methods and Programs in Biomedicine, 2010, 100(1):1-15. [13] Di CATALDO S, FICARRA E, ACQUAVIVA A, et al. Achieving the way for automated segmentation of nuclei in cancer tissue images through morphology-based approach:a quantitative evaluation[J]. Computerized Medical Imaging and Graphics, 2010, 34(6):453-461. [14] XU J, XIANG L, LIU Q, et al. Stacked Sparse AutoEncoder (SSAE) for nuclei detection on breast cancer histopathology images[J]. IEEE Transactions on Medical Imaging, 2015, PP(99):1. [15] CRUZ-ROA A, XU J, MADABHUSHI A. A note on the stability and discriminability of graph-based features for classification problems in digital pathology[C]//Proceedings of the 10th International Symposium on Medical Information Processing and Analysis, SPIE 9287. Bellingham:SPIE, 2015:928703. [16] LOMÉNIE N, RACOCEANU D. Point set morphological filtering and semantic spatial configuration modeling:Application to microscopic image and bio-structure analysis[J]. Pattern Recognition, 2012, 45(8):2894-2911. [17] DOYLE S, AGNER S, MADABHUSHI A, et al. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features[C]//ISBI 2008:Proceedings of the 5th IEEE International Symposium on Biomedical Imaging:from Nano to Macro. Piscataway:IEEE, 2008:496-499. [18] BASAVANHALLY A, GANESAN S, FELDMAN M D, et al. Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(8):2089-2099. [19] DOYLE S, HWANG M, SHAH K, et al. Automated grading of prostate cancer using architectural and textural image features[C]//ISBI 2007:Proceedings of the 4th IEEE International Symposium on Biomedical Imaging:from Nano to Macro. Piscataway:IEEE, 2007:1284-1287. [20] XU J, XIANG L, WANG G, et al. Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis[EB/OL].[2015-04-02]. http://dx.doi.org/10.1016/j.compmedimag. [21] KHAN A M, RAJPOOT N, TREANOR D, et al. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution[J]. IEEE Transactions on Biomedical Engineering, 2014, 61(6):1729-1738. [22] BARINOVA O, LEMPITSKY V, KHOLI P. On detection of multiple object instances using Hough transforms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(9):1773-1784. [23] WANG W, OZOLEK J A, ROHDE G K. Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images[J]. Cytometry Part A, 2010, 77(5):485-494. [24] BASAVANHALLY A, XU J, MADABHUSHI A, et al. Computer-aided prognosis of ER+breast cancer histopathology, and correlating survival outcome with oncotype DX assay[C]//ISBI'09:Proceedings of the 6th IEEE International Symposium on Biomedical Imaging:from Nano to Macro. Piscataway:IEEE, 2009:851-854. |