Digital image analysis in breast pathology—from image processing techniques to artificial intelligence

Translational Research

Volumn 194, Issue , 2018, Pages 19-35

  Robertson, Stephanie ^a   Azizpour, Hossein ^b,c   Smith, Kevin ^b,c   Hartman, Johan ^a,d  

^a KAROLINSKA INSTITUTE   (Sweden)

^b ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^c SCIENCE FOR LIFE LABORATORY   (Sweden)

^d STOCKHOLM SÖDER HOSPITAL   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

TUMOR MARKER;

ARTIFICIAL INTELLIGENCE; BREAST CANCER; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; DIGITAL IMAGING; HISTOPATHOLOGY; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MACHINE LEARNING; PRIORITY JOURNAL; REVIEW; BREAST; BREAST TUMOR; DIAGNOSTIC IMAGING; FEMALE; PATHOLOGY; PROCEDURES;

ARTIFICIAL INTELLIGENCE; BIOMARKERS, TUMOR; BREAST; BREAST NEOPLASMS; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MACHINE LEARNING;

EID: 85036635168     PISSN: 19315244     EISSN: 18781810     Source Type: Journal    
DOI: 10.1016/j.trsl.2017.10.010     Document Type: Review

References (187)

1. Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst, 2012, 1097–1105.
2. Hinton, G., Deng, L., Yu, D., et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29 (2012), 82–97.
3. Silver, D., Huang, A., Maddison, C.J., et al. Mastering the game of Go with deep neural networks and tree search. Nature 529 (2016), 484–489.
4. Esteva, A., Kuprel, B., Novoa, R.A., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542 (2017), 115–118.
5. Gulshan, V., Peng, L., Coram, M., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316 (2016), 2402–2410.
6. Syeda-Mahmood, T., Walach, E., Beymer, D., et al. Medical sieve: a cognitive assistant for radiologists and cardiologists. SPIE Medical Imaging. 2016 International Society for Optics and Photonics.
7. Anirudh, R., Thiagarajan, J.J., Bremer, T., Kim, H., Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data. SPIE Medical Imaging. 2016 International Society for Optics and Photonics.
8. Stewart, B., Wild, C., World cancer report 2014. Lyon: International Agency for Research on Cancer, 2014.
9. Siegel, R.L., Miller, K.D., Jemal, A., Cancer statistics, 2017. CA Cancer J Clin 67 (2017), 7–30.
10. Azzopardi, A.G., Problems in breast pathology. Classification of primary breast carcinoma. 1979, W.B. Saunders, Philadelphia.
11. Tavassoli, F.A., Pathology of the Breast. 1992, Appleton and Lange, Stanford.
12. Tavassoli, F.A., Devilee, P., World Health Organization classification of tumours. Tavassoli, F.A., Devilee, P., (eds.) Pathology and genetics of tumours of the breast and female genital organs, 2003, IARC Press, Lyon.
13. Lakhani, S.R., Lakhani, S.R., (eds.) WHO classification of tumours of the breast, 2012, IARC Press, Lyon.
14. Chu, P.G., Weiss, L.M., Keratin expression in human tissues and neoplasms. Histopathology 40 (2002), 403–439.
15. Perkins, W., Campbell, I., Leigh, I.M., MacKie, R.M., Keratin expression in normal skin and epidermal neoplasms demonstrated by a panel of monoclonal antibodies. J Cutan Pathol 19 (1992), 476–482.
16. Qureshi, H.S., Linden, M.D., Divine, G., Raju, U.B., E-cadherin status in breast cancer correlates with histologic type but does not correlate with established prognostic parameters. Am J Clin Pathol 125 (2006), 377–385.
17. Berezowski, K., Stastny, J.F., Kornstein, M.J., Cytokeratins 7 and 20 and carcinoembryonic antigen in ovarian and colonic carcinoma. Mod Pathol 9 (1996), 426–429.
18. Lagendijk, J.H., Mullink, H., Van Diest, P.J., Meijer, G.A., Meijer, C.J., Tracing the origin of adenocarcinomas with unknown primary using immunohistochemistry: differential diagnosis between colonic and ovarian carcinomas as primary sites. Hum Pathol 29 (1998), 491–497.
19. Loy, T.S., Calaluce, R.D., Utility of cytokeratin immunostaining in separating pulmonary adenocarcinomas from colonic adenocarcinomas. Am J Clin Pathol 102 (1994), 764–767.
20. Nadji, M., Tabei, S.Z., Castro, A., et al. Prostatic-specific antigen: an immunohistologic marker for prostatic neoplasms. Cancer 48 (1981), 1229–1232.
21. Gould, V.E., Synaptophysin. A new and promising pan-neuroendocrine marker. Arch Pathol Lab Med 111 (1987), 791–794.
22. Cao, Y., Schlag, P.M., Karsten, U., Immunodetection of epithelial mucin (MUC1, MUC3) and mucin-associated glycotopes (TF, Tn, and sialosyl-Tn) in benign and malignant lesions of colonic epithelium: apolar localization corresponds to malignant transformation. Virchows Arch 431 (1997), 159–166.
23. Naber, S.P., Molecular pathology—diagnosis of infectious disease. N Engl J Med 331 (1994), 1212–1215.
24. Scarff, R.W., Torloni, H., Histological typing of breast tumours (International Histological Classification of Tumours, no. 2). Geneva: World Health Organization, 1968.
25. Elston, C.W., Ellis, I.O., Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19 (1991), 403–410.
26. Meyer, J.S., Alvarez, C., Milikowski, C., et al. Breast carcinoma malignancy grading by Bloom-Richardson system vs proliferation index: reproducibility of grade and advantages of proliferation index. Mod Pathol 18 (2005), 1067–1078.
27. Brierley, J.D., Gospodarowicz, M.K., Wittekind, C., Brierley, J.D., Gospodarowicz, M.K., Wittekind, C., (eds.) TNM classification of malignant tumours, 8th ed, 2017, John Wiley & Sons, Ltd, Oxford, UK.
28. Coates, A.S., Winer, E.P., Goldhirsch, A., et al. Tailoring therapies—improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann Oncol 26 (2015), 1533–1546.
29. Goldhirsch, A., Winer, E.P., Coates, A.S., et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 24 (2013), 2206–2223.
30. Goldhirsch, A., Wood, W.C., Coates, A.S., Gelber, R.D., Thurlimann, B., Senn, H.J., Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 22 (2011), 1736–1747.
31. Harris, L., Fritsche, H., Mennel, R., et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 25 (2007), 5287–5312.
32. Van Poznak, C., Somerfield, M.R., Bast, R.C., et al. Use of biomarkers to guide decisions on systemic therapy for women with metastatic breast cancer: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol 33 (2015), 2695–2704.
33. Harris, L.N., Ismaila, N., McShane, L.M., et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol 34 (2016), 1134–1150.
34. Hammond, M.E., Hayes, D.F., Dowsett, M., et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. Arch Pathol Lab Med 134 (2010), 907–922.
35. Hawkins, R.A., Roberts, M.M., Forrest, A.P., Oestrogen receptors and breast cancer: current status. Br J Surg 67 (1980), 153–169.
36. Barnes, D.M., Hanby, A.M., Oestrogen and progesterone receptors in breast cancer: past, present and future. Histopathology 38 (2001), 271–274.
37. Li, C.I., Daling, J.R., Malone, K.E., Incidence of invasive breast cancer by hormone receptor status from 1992 to 1998. J Clin Oncol 21 (2003), 28–34.
38. Slamon, D.J., Clark, G.M., Wong, S.G., Levin, W.J., Ullrich, A., McGuire, W.L., Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235 (1987), 177–182.
39. Wolff, A.C., Hammond, M.E., Hicks, D.G., et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol 31 (2013), 3997–4013.
40. Grabau, D., Quality- and Standardization Committee (KVAST). Quality- and standadization document for breast tumors. Alingsås, Sweden: Swedish Pathological Society/Swedish Society of Clinical Cytology, 2014.
41. Gavrielides, M.A., Gallas, B.D., Lenz, P., Badano, A., Hewitt, S.M., Observer variability in the interpretation of HER2/neu immunohistochemical expression with unaided and computer-aided digital microscopy. Arch Pathol Lab Med 135 (2011), 233–242.
42. Bloom, K., Harrington, D., Enhanced accuracy and reliability of HER-2/neu immunohistochemical scoring using digital microscopy. Am J Clin Pathol 121 (2004), 620–630.
43. Roche, P.C., Suman, V.J., Jenkins, R.B., et al. Concordance between local and central laboratory HER2 testing in the breast intergroup trial N9831. J Natl Cancer Inst 94 (2002), 855–857.
44. Perez, E.A., Suman, V.J., Davidson, N.E., et al. HER2 testing by local, central, and reference laboratories in specimens from the North Central Cancer Treatment Group N9831 intergroup adjuvant trial. J Clin Oncol 24 (2006), 3032–3038.
45. Bueno-de-Mesquita, J.M., Nuyten, D.S., Wesseling, J., van Tinteren, H., Linn, S.C., van de Vijver, M.J., The impact of inter-observer variation in pathological assessment of node-negative breast cancer on clinical risk assessment and patient selection for adjuvant systemic treatment. Ann Oncol 21 (2010), 40–47.
46. Dowsett, M., Nielsen, T.O., A'Hern, R., et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst 103 (2011), 1656–1664.
47. Criscitiello, C., Disalvatore, D., De Laurentiis, M., et al. High Ki-67 score is indicative of a greater benefit from adjuvant chemotherapy when added to endocrine therapy in luminal B HER2 negative and node-positive breast cancer. Breast 23 (2014), 69–75.
48. Curigliano, G., Burstein, H.J., P Winer, E., et al. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann Oncol 28 (2017), 1700–1712.
49. de Azambuja, E., Cardoso, F., de Castro, G. Jr, et al. Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12,155 patients. Br J Cancer 96 (2007), 1504–1513.
50. Polley, M.Y., Leung, S.C., Gao, D., et al. An international study to increase concordance in Ki67 scoring. Mod Pathol 28 (2015), 778–786.
51. Hayes, D.F., From genome to bedside: are we lost in translation?. Breast 22:Suppl 2 (2013), S22–S26.
52. Polley, M.Y., Leung, S.C., McShane, L.M., et al. An international Ki67 reproducibility study. J Natl Cancer Inst 105 (2013), 1897–1906.
53. Varga, Z., Diebold, J., Dommann-Scherrer, C., et al. How reliable is Ki-67 immunohistochemistry in grade 2 breast carcinomas? A QA study of the Swiss Working Group of Breast- and Gynecopathologists. PLoS ONE, 7, 2012 e37379.
54. van ‘t Veer, L.J., Dai, H., van de Vijver, M.J., et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415 (2002), 530–536.
55. Sorlie, T., Perou, C.M., Tibshirani, R., et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98 (2001), 10869–10874.
56. Loi, S., Haibe-Kains, B., Desmedt, C., et al. Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol 25 (2007), 1239–1246.
57. Parker, J.S., Mullins, M., Cheang, M.C., et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27 (2009), 1160–1167.
58. Perou, C.M., Sorlie, T., Eisen, M.B., et al. Molecular portraits of human breast tumours. Nature 406 (2000), 747–752.
59. Fan, C., Oh, D.S., Wessels, L., et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 355 (2006), 560–569.
60. Hu, Z., Fan, C., Oh, D.S., et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics, 7, 2006, 96.
61. Sorlie, T., Tibshirani, R., Parker, J., et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100 (2003), 8418–8423.
62. Bastien, R.R., Rodriguez-Lescure, A., Ebbert, M.T., et al. PAM50 breast cancer subtyping by RT-qPCR and concordance with standard clinical molecular markers. BMC Med Genomics, 5, 2012, 44.
63. Nielsen, T.O., Parker, J.S., Leung, S., et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin Cancer Res 16 (2010), 5222–5232.
64. Bayraktar, S., Royce, M., Stork-Sloots, L., de Snoo, F., Gluck, S., Molecular subtyping predicts pathologic tumor response in early-stage breast cancer treated with neoadjuvant docetaxel plus capecitabine with or without trastuzumab chemotherapy. Med Oncol, 31, 2014, 163.
65. Nguyen, B., Cusumano, P.G., Deck, K., et al. Comparison of molecular subtyping with BluePrint, MammaPrint, and TargetPrint to local clinical subtyping in breast cancer patients. Ann Surg Oncol 19 (2012), 3257–3263.
66. Dowsett, M., Sestak, I., Lopez-Knowles, E., et al. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol 31 (2013), 2783–2790.
67. Paik, S., Shak, S., Tang, G., et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351 (2004), 2817–2826.
68. Cheang, M.C., Chia, S.K., Voduc, D., et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 101 (2009), 736–750.
69. Guiu, S., Michiels, S., Andre, F., et al. Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement. Ann Oncol 23 (2012), 2997–3006.
70. de Azambuja, E., Holmes, A.P., Piccart-Gebhart, M., et al. Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): survival outcomes of a randomised, open-label, multicentre, phase 3 trial and their association with pathological complete response. Lancet Oncol 15 (2014), 1137–1146.
71. Ho, J., Parwani, A.V., Jukic, D.M., Yagi, Y., Anthony, L., Gilbertson, J.R., Use of whole slide imaging in surgical pathology quality assurance: design and pilot validation studies. Hum Pathol 37 (2006), 322–331.
72. Helin, H., Lundin, M., Lundin, J., et al. Web-based virtual microscopy in teaching and standardizing Gleason grading. Hum Pathol 36 (2005), 381–386.
73. Fine, J.L., Grzybicki, D.M., Silowash, R., et al. Evaluation of whole slide image immunohistochemistry interpretation in challenging prostate needle biopsies. Hum Pathol 39 (2008), 564–572.
74. Lopez, A.M., Graham, A.R., Barker, G.P., et al. Virtual slide telepathology enables an innovative telehealth rapid breast care clinic. Hum Pathol 40 (2009), 1082–1091.
75. Velez, N., Jukic, D., Ho, J., Evaluation of 2 whole-slide imaging applications in dermatopathology. Hum Pathol 39 (2008), 1341–1349.
76. Evans, A.J., Chetty, R., Clarke, B.A., et al. Primary frozen section diagnosis by robotic microscopy and virtual slide telepathology: the University Health Network experience. Hum Pathol 40 (2009), 1070–1081.
77. Wienert, S., Beil, M., Saeger, K., Hufnagl, P., Schrader, T., Integration and acceleration of virtual microscopy as the key to successful implementation into the routine diagnostic process. Diagn Pathol, 4, 2009, 3.
78. Jara-Lazaro, A.R., Thamboo, T.P., Teh, M., Tan, P.H., Digital pathology: exploring its applications in diagnostic surgical pathology practice. Pathology 42 (2010), 512–518.
79. Warren Burhenne, L.J., Wood, S.A., D'Orsi, C.J., et al. Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology 215 (2000), 554–562.
80. Shiraishi, J., Li, Q., Appelbaum, D., Doi, K., Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin Nucl Med 41 (2011), 449–462.
81. Khan, A.M., Rajpoot, N., Treanor, D., Magee, D., A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng 61 (2014), 1729–1738.
82. Kass, M., Witkin, A., Terzopoulos, D., Snakes: active contour models. Int J Comput Vis 1 (1988), 321–331.
83. Jing, J., Wan, T., Cao, J., Qin, Z., An improved hybrid active contour model for nuclear segmentation on breast cancer histopathology. Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016 IEEE.
84. Ali, S., Madabhushi, A., Active contour for overlap resolution using watershed based initialization (ACOReW): Applications to histopathology. Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, 2011 IEEE.
85. Ali, S., Madabhushi, A., An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE Trans Med Imaging 31 (2012), 1448–1460.
86. Veta, M., Pluim, J.P., van Diest, P.J., Viergever, M.A., Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng 61 (2014), 1400–1411.
87. Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B., Histopathological image analysis: a review. IEEE Rev Biomed Eng 2 (2009), 147–171.
88. Tuominen, V.J., Ruotoistenmaki, S., Viitanen, A., Jumppanen, M., Isola, J., ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res, 12, 2010, 12.
89. Yeo, M.K., Kim, H.E., Kim, S.H., Chae, B.J., Song, B.J., Lee, A., Clinical usefulness of the free web-based image analysis application ImmunoRatio for assessment of Ki-67 labelling index in breast cancer. J Clin Pathol 70 (2017), 715–719.
90. Tuominen, V.J., Tolonen, T.T., Isola, J., ImmunoMembrane: a publicly available web application for digital image analysis of HER2 immunohistochemistry. Histopathology 60 (2012), 758–767.
91. Stalhammar, G., Fuentes Martinez, N., Lippert, M., et al. Digital image analysis outperforms manual biomarker assessment in breast cancer. Mod Pathol 29 (2016), 318–329.
92. Lykkegaard Andersen, N., Brugmann, A., Lelkaitis, G., Nielsen, S., Friis Lippert, M., Vyberg, M., Virtual Double Staining: a digital approach to immunohistochemical quantification of estrogen receptor protein in breast carcinoma specimens. Appl Immunohistochem Mol Morphol, 2017, 10.1097/PAI.0000000000000502.
93. Roge, R., Riber-Hansen, R., Nielsen, S., Vyberg, M., Proliferation assessment in breast carcinomas using digital image analysis based on virtual Ki67/cytokeratin double staining. Breast Cancer Res Treat 158 (2016), 11–19.
94. Ahern, T.P., Beck, A.H., Rosner, B.A., et al. Continuous measurement of breast tumour hormone receptor expression: a comparison of two computational pathology platforms. J Clin Pathol 70 (2017), 428–434.
95. Holten-Rossing, H., Moller Talman, M.L., Kristensson, M., Vainer, B., Optimizing HER2 assessment in breast cancer: application of automated image analysis. Breast Cancer Res Treat 152 (2015), 367–375.
96. Rizzardi, A.E., Johnson, A.T., Vogel, R.I., et al. Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring. Diagn Pathol, 7, 2012, 42.
97. Nielsen, P.S., Riber-Hansen, R., Jensen, T.O., Schmidt, H., Steiniche, T., Proliferation indices of phosphohistone H3 and Ki67: strong prognostic markers in a consecutive cohort with stage I/II melanoma. Mod Pathol 26 (2013), 404–413.
98. Brugmann, A., Eld, M., Lelkaitis, G., et al. Digital image analysis of membrane connectivity is a robust measure of HER2 immunostains. Breast Cancer Res Treat 132 (2012), 41–49.
99. Holten-Rossing, H., Talman, M.M., Jylling, A.M.B., Laenkholm, A.V., Kristensson, M., Vainer, B., Application of automated image analysis reduces the workload of manual screening of sentinel Lymph node biopsies in breast cancer. Histopathology, 2017, 10.1111/his.13305.
100. Konsti, J., Lundin, M., Linder, N., et al. Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium. Diagn Pathol, 7, 2012, 29.
101. Buchanan, B.G., Shortliffe, E.H., Rule-based expert systems: Addison-Wesley Reading, MA. 1984.
102. Olson, W.H., Kaemmerer, W.F., Prioritized rule based method and apparatus for diagnosis and treatment of arrhythmias. Google Patents, 1996.
103. Kramer, M.A., Palowitch, B., A rule-based approach to fault diagnosis using the signed directed graph. AIChE J 33 (1987), 1067–1078.
104. Xu, J., Rule-based automatic software performance diagnosis and improvement. Perform Eval 69 (2012), 525–550.
105. Azimi, V., Chang, Y.H., Thibault, G., et al. Breast cancer histopathology image analysis pipeline for tumor purity estimation. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, 2017 IEEE.
106. Ehteshami Bejnordi, B., Linz, J., Glass, B., et al. Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. arXiv preprint arXiv:170205803, 2017.
107. Song, Y., Zou, J.J., Chang, H., Cai, W., Adapting fisher vectors for histopathology image classification. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, 2017 IEEE.
108. Das, K., Karri, S.P.K., Roy, A.G., Chatterjee, J., Sheet, D., Classifying histopathology whole-slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, 2017 IEEE.
109. Wollmann, T., Rohr, K., Deep residual Hough voting for mitotic cell detection in histopathology images. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, 2017 IEEE.
110. Naylor, P., Laé M., Reyal, F., Walter, T., Nuclei segmentation in histopathology images using deep neural networks. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, 2017 IEEE.
111. Cao, J., Qin, Z., Jing, J., Chen, J., Wan, T., An automatic breast cancer grading method in histopathological images based on pixel-, object-, and semantic-level features. Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016 IEEE.
112. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L., Breast cancer histopathological image classification using convolutional neural networks. Neural Networks (IJCNN), 2016 International Joint Conference on, 2016 IEEE.
113. Li, W., Zhang, J., McKenna, S.J., Multiple instance cancer detection by boosting regularised trees. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015 Springer.
114. Paul, A., Dey, A., Mukherjee, D.P., Sivaswamy, J., Tourani, V., Regenerative random forest with automatic feature selection to detect mitosis in histopathological breast cancer images. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015 Springer.
115. Xie, Y., Xing, F., Kong, X., Su, H., Yang, L., Beyond classification: structured regression for robust cell detection using convolutional neural network. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015 Springer.
116. Su, H., Xing, F., Kong, X., Xie, Y., Zhang, S., Yang, L., Robust cell detection and segmentation in histopathological images using sparse reconstruction and stacked denoising autoencoders. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015 Springer.
117. Xie, Y., Kong, X., Xing, F., Liu, F., Su, H., Yang, L., Deep voting: A robust approach toward nucleus localization in microscopy images. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015 Springer.
118. Akbar, S., Jordan, L., Thompson, A.M., McKenna, S.J., Tumor localization in tissue microarrays using rotation invariant superpixel pyramids. Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 2015 IEEE.
119. Su, H., Liu, F., Xie, Y., Xing, F., Meyyappan, S., Yang, L., Region segmentation in histopathological breast cancer images using deep convolutional neural network. Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 2015 IEEE.
120. Peter, L., Mateus, D., Chatelain, P., et al. Leveraging random forests for interactive exploration of large histological images. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014 Springer.
121. Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J., Mitosis detection in breast cancer histology images with deep neural networks. Med Image Comput Comput Assist Interv 16 (2013), 411–418.
122. Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A., Learning to detect cells using non-overlapping extremal regions. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2012 Springer.
123. Song, Y., Li, Q., Huang, H., Feng, D., Chen, M., Cai, W., Histopathology Image Categorization with Discriminative Dimension Reduction of Fisher Vectors. European Conference on Computer Vision, 2016 Springer.
124. Vu, T.H., Mousavi, H.S., Monga, V., Rao, U.A., Rao, G., DFDL: Discriminative feature-oriented dictionary learning for histopathological image classification. Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 2015 IEEE.
125. Khan, A.M., Sirinukunwattana, K., Rajpoot, N., Geodesic geometric mean of regional covariance descriptors as an image-level descriptor for nuclear atypia grading in breast histology images. International Workshop on Machine Learning in Medical Imaging, 2014 Springer.
126. Srinivas, U., Mousavi, H., Jeon, C., Monga, V., Hattel, A., Jayarao, B., SHIRC: A simultaneous sparsity model for histopathological image representation and classification. Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, 2013 IEEE.
127. Xu, Y., Zhu, J.-Y., Chang, E., Tu, Z., Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012 IEEE.
128. Xu, Y., Zhang, J., Eric, I., Chang, C., Lai, M., Tu, Z., Context-constrained multiple instance learning for histopathology image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2012 Springer.
129. Cheplygina, V., Sørensen, L., Tax, D.M., de Bruijne, M., Loog, M., Label stability in multiple instance learning. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015 Springer.
130. Chang, H., Zhou, Y., Spellman, P., Parvin, B., Stacked predictive sparse coding for classification of distinct regions in tumor histopathology. Proceedings of the IEEE International Conference on Computer Vision, 2013.
131. Lowe, D.G., Object recognition from local scale-invariant features. Computer vision, 1999 The proceedings of the seventh IEEE international conference on, 1999 Ieee.
132. Bay, H., Tuytelaars, T., Van Gool, L., Surf: Speeded up robust features. Computer vision–ECCV 2006, 2006, 404–417.
133. Ojala, T., Pietikainen, M., Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24 (2002), 971–987.
134. Cheikh, B.B., Elie, N., Plancoulaine, B., Bor-Angelier, C., Racoceanu, D., Spatial interaction analysis with graph based mathematical morphology for histopathology. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on, 2017 IEEE.
135. Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J., Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. Biomedical Imaging: From Nano to Macro, 2008 ISBI 2008 5th IEEE International Symposium on, 2008 IEEE.
136. Xu, J., Xiang, L., Liu, Q., et al. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35 (2016), 119–130.
137. Cruz-Roa, A.A., Ovalle, J.E.A., Madabhushi, A., Osorio, F.A.G., A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013 Springer.
138. Nayak, N., Chang, H., Borowsky, A., Spellman, P., Parvin, B., Classification of tumor histopathology via sparse feature learning. Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, 2013 IEEE.
139. Quinlan, J.R., Induction of decision trees. Machine learning 1 (1986), 81–106.
140. Breiman, L., Random forests. Machine learning 45 (2001), 5–32.
141. Liaw, A., Wiener, M., Classification and regression by randomForest. R News 2 (2002), 18–22.
142. Cortes, C., Vapnik, V., Support-vector networks. Mach Learn 20 (1995), 273–297.
143. Weston, J., Watkins, C., Multi-class support vector machines. Technical Report CSD-TR-98-04, Department of Computer Science, Royal Holloway, University of London, May, 1998.
144. LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444.
145. Bengio, Y., Courville, A., Vincent, P., Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35 (2013), 1798–1828.
146. LeCun, Y., Kavukcuoglu, K., Farabet, C., Convolutional networks and applications in vision. Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, 2010 IEEE.
147. Kingma, D.P., Welling, M., Auto-encoding variational Bayes. arXiv preprint arXiv:13126114, 2013.
148. Graves, A., Supervised sequence labelling with recurrent neural networks. 2012, Springer.
149. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. Generative adversarial nets. Adv Neural Inf Process Syst, 2014.
150. Bayramoglu, N., Heikkilä J., Transfer learning for cell nuclei classification in histopathology images. Computer Vision–ECCV 2016 Workshops, 2016 Springer.
151. Veta, M., Van Diest, P.J., Pluim, J.P., Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016 Springer.
152. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A., Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. AAAI, 2017.
153. He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
154. Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556, 2014.
155. Ciregan, D., Meier, U., Schmidhuber, J., Multi-column deep neural networks for image classification. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012 IEEE.
156. Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L., Densely connected convolutional networks. arXiv preprint arXiv:160806993, 2016.
157. Hinton, G.E., Deep belief networks. Scholarpedia, 4, 2009, 5947.
158. Salakhutdinov, R., Hinton, G., Deep boltzmann machines. Artificial Intelligence and Statistics, 2009.
159. Huang, R., Zhang, S., Li, T., He, R., Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis. arXiv preprint arXiv:170404086, 2017.
160. Lafarge, M.W., Pluim, J.P., Eppenhof, K.A., Moeskops, P., Veta, M., Domain-adversarial neural networks to address the appearance variability of histopathology images. arXiv preprint arXiv:170706183, 2017.
161. Udrea, A., Mitra, G.D., Generative Adversarial Neural Networks for Pigmented and Non-Pigmented Skin Lesions Detection in Clinical Images. Control Systems and Computer Science (CSCS), 2017 21st International Conference on, 2017 IEEE.
162. Dalle, J.R., Leow, W.K., Racoceanu, D., Tutac, A.E., Putti, T.C., Automatic breast cancer grading of histopathological images. Conf Proc IEEE Eng Med Biol Soc 2008 (2008), 3052–3055.
163. Petushi, S., Garcia, F.U., Haber, M.M., Katsinis, C., Tozeren, A., Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Med Imaging, 6, 2006, 14.
164. Basavanhally, A., Ganesan, S., Feldman, M., et al. Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides. IEEE Trans Biomed Eng 60 (2013), 2089–2099.
165. Wang, H., Cruz-Roa, A., Basavanhally, A., et al. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging (Bellingham), 1, 2014, 034003.
166. Veta, M., van Diest, P.J., Willems, S.M., et al. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal 20 (2015), 237–248.
167. Malon, C.D., Cosatto, E., Classification of mitotic figures with convolutional neural networks and seeded blob features. J Pathol Inform, 4, 2013, 9.
168. Irshad, H., Automated mitosis detection in histopathology using morphological and multi-channel statistics features. J Pathol Inform, 4, 2013, 10.
169. Roux, L., Racoceanu, D., Lomenie, N., et al. Mitosis detection in breast cancer histological images an ICPR 2012 contest. J Pathol Inform, 4, 2013, 8.
170. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L., A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63 (2016), 1455–1462.
171. Cruz-Roa, A., Basavanhally, A., Gonzalez, F., et al. Automatic detection of invasive ductal carcinoma in whole slide images with Convolutional Neural Networks. In: Gurcan MN, Madabhushi A, editors. Medical Imaging 2014: Digital Pathology. Proceedings of SPIE. 90412014, 2014.
172. Araujo, T., Aresta, G., Castro, E., et al. Classification of breast cancer histology images using Convolutional Neural Networks. PLoS ONE, 12, 2017 e0177544.
173. Dundar, M.M., Badve, S., Bilgin, G., et al. Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans Biomed Eng 58 (2011), 1977–1984.
174. Cruz-Roa, A., Gilmore, H., Basavanhally, A., et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: a Deep Learning approach for quantifying tumor extent. Sci Rep, 7, 2017, 46450.
175. Litjens, G., Sanchez, C.I., Timofeeva, N., et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep, 6, 2016, 26286.
176. Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H., Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:160605718, 2016.
177. Liu, Y., Gadepalli, K., Norouzi, M., et al. Detecting cancer metastases on gigapixel pathology images. arXiv preprint arXiv:170302442, 2017.
178. Wollmann, T., Rohr, K., Automatic breast cancer grading in lymph nodes using a deep neural network. arXiv preprint arXiv:170707565, 2017.
179. Vandenberghe, M.E., Scott, M.L., Scorer, P.W., Soderberg, M., Balcerzak, D., Barker, C., Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep, 7, 2017, 45938.
180. Mungle, T., Tewary, S., Das, D.K., et al. MRF-ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images. J Microsc 267 (2017), 117–129.
181. Turkki, R., Linder, N., Kovanen, P.E., Pellinen, T., Lundin, J., Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. J Pathol Inform, 7, 2016, 38.
182. Chen, J.M., Li, Y., Xu, J., et al. Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: a review. Tumour Biol, 39, 2017 1010428317694550.
183. Madabhushi, A., Agner, S., Basavanhally, A., Doyle, S., Lee, G., Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data. Comput Med Imaging Graph 35 (2011), 506–514.
184. Beck, A.H., Sangoi, A.R., Leung, S., et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med, 3, 2011, 108ra13.
185. Szegedy, C., Zaremba, W., Sutskever, I., et al. Intriguing properties of neural networks. arXiv preprint arXiv:13126199, 2013.
186. Xie, P., Bilenko, M., Finley, T., Gilad-Bachrach, R., Lauter, K., Naehrig, M., Crypto-nets: Neural networks over encrypted data. arXiv preprint arXiv:14126181, 2014.
187. Collins, F.S., Varmus, H., A new initiative on precision medicine. N Engl J Med 372 (2015), 793–795.