{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T09:24:23Z","timestamp":1774689863513,"version":"3.50.1"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T00:00:00Z","timestamp":1580083200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T00:00:00Z","timestamp":1580083200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s10278-019-00295-z","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T22:02:13Z","timestamp":1580162533000},"page":"1091-1121","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4360-0092","authenticated-orcid":false,"given":"Asha","family":"Das","sequence":"first","affiliation":[]},{"given":"Madhu S.","family":"Nair","sequence":"additional","affiliation":[]},{"given":"S. David","family":"Peter","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,27]]},"reference":[{"key":"295_CR1","doi-asserted-by":"publisher","unstructured":"Aksac A, Demetrick DJ, Ozyer T, Alhajj R (2019) BrecaHAD: A dataset for breast cancer histopathological annotation and diagnosis. BMC Research Notes 12(1). https:\/\/doi.org\/10.1186\/s13104-019-4121-7","DOI":"10.1186\/s13104-019-4121-7"},{"key":"295_CR2","doi-asserted-by":"publisher","unstructured":"Alom MZ, Yakopcic C, Nasrin MS, Taha TM, Asari VK (2019) Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network. Journal of Digital Imaging. https:\/\/doi.org\/10.1007\/s10278-019-00182-7","DOI":"10.1007\/s10278-019-00182-7"},{"key":"295_CR3","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, Pol\u00f3nia A, Campilho A Classification of breast cancer histology images using convolutional neural networks. PloS one 12(6), 2017","DOI":"10.1371\/journal.pone.0177544"},{"key":"295_CR4","doi-asserted-by":"publisher","first-page":"24,680","DOI":"10.1109\/ACCESS.2018.2831280","volume":"6","author":"D Bardou","year":"2018","unstructured":"Bardou D, Zhang K, Ahmad SM: Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 6: 24,680\u201324,693, 2018","journal-title":"IEEE Access"},{"issue":"8","key":"295_CR5","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1109\/TBME.2013.2245129","volume":"60","author":"A Basavanhally","year":"2013","unstructured":"Basavanhally A, Ganesan S, Feldman M, Shih N, Mies C, Tomaszewski J, Madabhushi A: Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides. IEEE Transactions on Biomedical Engineering 60 (8): 2089\u20132099, 2013. https:\/\/doi.org\/10.1109\/TBME.2013.2245129","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"295_CR6","doi-asserted-by":"publisher","unstructured":"Bayramoglu N, Kannala J, Heikkila J (2017) Deep learning for magnification independent breast cancer histopathology image classification. In: Proceedings International Conference on Pattern Recognition, pp 2440\u20132445. https:\/\/doi.org\/10.1109\/ICPR.2016.7900002","DOI":"10.1109\/ICPR.2016.7900002"},{"issue":"4","key":"295_CR7","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.bbe.2016.06.005","volume":"36","author":"KS Beevi","year":"2016","unstructured":"Beevi KS, Nair MS, Bindu GR: Automatic segmentation of cell nuclei using Krill Herd optimization based multi-thresholding and Localized Active Contour Model. Biocybernetics and Biomedical Engineering 36 (4): 584\u2013596, 2016. https:\/\/doi.org\/10.1016\/j.bbe.2016.06.005","journal-title":"Biocybernetics and Biomedical Engineering"},{"issue":"4","key":"295_CR8","doi-asserted-by":"publisher","first-page":"044,504","DOI":"10.1117\/1.JMI.4.4.044504","volume":"4","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi BE, Zuidhof G, Balkenhol M, Hermsen M, Bult P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak J: Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. Journal of Medical Imaging 4 (4): 044,504, 2017","journal-title":"Journal of Medical Imaging"},{"issue":"9","key":"295_CR9","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1200\/JCO.1987.5.9.1378","volume":"5","author":"G Contesso","year":"1987","unstructured":"Contesso G, Mouriesse H, Friedman S, Genin J, Sarrazin D, Rouesse J: The importance of histologic grade in long-term prognosis of breast cancer: a study of 1,010 patients, uniformly treated at the Institut Gustave-Roussy. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 5 (9): 1378\u20131386, 1987","journal-title":"Journal of clinical oncology : official journal of the American Society of Clinical Oncology"},{"key":"295_CR10","doi-asserted-by":"publisher","unstructured":"Cosatto E, Miller M, Graf HP, Meyer JS (2008) Grading nuclear pleomorphism on histological micrographs. 2008 ICPR 2008 19th International Conference on (August 2016) Pattern Recognition, pp 1\u20134. https:\/\/doi.org\/10.1109\/ICPR.2008.4761112","DOI":"10.1109\/ICPR.2008.4761112"},{"key":"295_CR11","doi-asserted-by":"publisher","unstructured":"Dalle JR, Leow WK, Racoceanu D, Tutac AE, Putti TC (2008) Automatic breast cancer grading of histopathological images. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 3052\u20133055. https:\/\/doi.org\/10.1109\/IEMBS.2008.4649847","DOI":"10.1109\/IEMBS.2008.4649847"},{"key":"295_CR12","unstructured":"Dalle Jr, Racoceanu D, Putti TC Nuclear pleomorphism scoring by selective cell nuclei detection. IEEE Workshop on Applications of Computer Vision: 7\u20138, 2009"},{"issue":"3","key":"295_CR13","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1109\/TIP.2018.2877337","volume":"28","author":"A Das","year":"2019","unstructured":"Das A, Nair MS, Peter SD: Sparse representation over learned dictionaries on the riemannian manifold for automated grading of nuclear pleomorphism in breast cancer. IEEE Transactions on Image Processing 28 (3): 1248\u20131260, 2019","journal-title":"IEEE Transactions on Image Processing"},{"key":"295_CR14","doi-asserted-by":"publisher","unstructured":"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. In: 2008 5Th IEEE international symposium on biomedical imaging: From nano to macro, Proceedings, ISBI, 2008, pp 496\u2013499. https:\/\/doi.org\/10.1109\/ISBI.2008.4541041","DOI":"10.1109\/ISBI.2008.4541041"},{"issue":"1","key":"295_CR15","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.acha.2007.09.003","volume":"25","author":"G Easley","year":"2008","unstructured":"Easley G, Labate D, Lim WQ: Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis 25 (1): 25\u201346, 2008. https:\/\/doi.org\/10.1016\/j.acha.2007.09.003","journal-title":"Applied and Computational Harmonic Analysis"},{"issue":"11","key":"295_CR16","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1001\/jama.2015.1405","volume":"313","author":"JG Elmore","year":"2015","unstructured":"Elmore JG, Longton GM, Carney PA, Geller BM, Onega T, Tosteson ANA, Nelson HD, Pepe MS, Allison KH, Schnitt SJ, O\u2019Malley FP, Weaver DL: Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens. JAMA 313 (11): 1122, 2015. https:\/\/doi.org\/10.1001\/jama.2015.1405, 15334406","journal-title":"JAMA"},{"key":"295_CR17","doi-asserted-by":"publisher","unstructured":"Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long term followup, vol 19. https:\/\/doi.org\/10.1111\/j.1365-2559.1991.tb00229.x, arXiv:1011.1669v3","DOI":"10.1111\/j.1365-2559.1991.tb00229.x"},{"key":"295_CR18","unstructured":"Faridi P, Danyali H, Helfroush MS, Jahromi MA (2016) Cancerous nuclei detection and scoring in breast cancer histopathological images. arXiv:161201237"},{"issue":"8","key":"295_CR19","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T: An introduction to ROC analysis. Pattern Recognition Letters 27 (8): 861\u2013874, 2006. https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recognition Letters"},{"issue":"10010","key":"295_CR20","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1016\/S0140-6736(15)00128-2","volume":"386","author":"MH Forouzanfar","year":"2015","unstructured":"Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S, Brauer M, Burnett R, Casey D, Coates MM, Cohen A, et al: Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990\u20132013: a systematic analysis for the global burden of disease study 2013. The Lancet 386 (10010): 2287\u20132323, 2015","journal-title":"The Lancet"},{"key":"295_CR21","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.artmed.2018.04.005","volume":"88","author":"Z Gandomkar","year":"2018","unstructured":"Gandomkar Z, Brennan PC, Mello-Thoms C: MudeRN: Multi-category classification of breast histopathological image using deep residual networks. Artificial Intelligence in Medicine 88: 14\u201324, 2018. https:\/\/doi.org\/10.1016\/j.artmed.2018.04.005","journal-title":"Artificial Intelligence in Medicine"},{"key":"295_CR22","doi-asserted-by":"publisher","unstructured":"Gandomkar Z, Brennan PC, Mello-Thoms C (2019) Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study. Journal of Digital Imaging. https:\/\/doi.org\/10.1007\/s10278-019-00181-8","DOI":"10.1007\/s10278-019-00181-8"},{"issue":"1B","key":"295_CR23","first-page":"571","volume":"18","author":"C Genestie","year":"1998","unstructured":"Genestie C, Zafrani B, Asselain B, Fourquet A, Rozan S, Validire P, Vincent-Salomon A, Sastre-Garau X: Comparison of the prognostic value of Scarff-Bloom-Richardson and Nottingham histological grades in a series of 825 cases of breast cancer: major importance of the mitotic count as a component of both grading systems. Anticancer Research 18 (1B): 571\u2013576, 1998","journal-title":"Anticancer Research"},{"issue":"1","key":"295_CR24","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1146\/annurev-pathol-011811-120902","volume":"8","author":"F Ghaznavi","year":"2013","unstructured":"Ghaznavi F, Evans A, Madabhushi A, Feldman M: Digital imaging in pathology: Whole-slide imaging and beyond. Annual Review of Pathology: Mechanisms of Disease 8 (1): 331\u2013359, 2013. https:\/\/doi.org\/10.1146\/annurev-pathol-011811-120902","journal-title":"Annual Review of Pathology: Mechanisms of Disease"},{"key":"295_CR25","doi-asserted-by":"publisher","unstructured":"Golatkar A, Anand D, Sethi A (2018) Classification of Breast Cancer Histology Using Deep Learning. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 10882 LNCS, pp 837\u2013844. https:\/\/doi.org\/10.1007\/978-3-319-93000-8_95","DOI":"10.1007\/978-3-319-93000-8_95"},{"key":"295_CR26","doi-asserted-by":"publisher","unstructured":"Guo Y, Dong H, Song F, Zhu C, Liu J (2018) Breast Cancer Histology Image Classification Based on Deep Neural Networks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 10882 LNCS, pp 827\u2013836. https:\/\/doi.org\/10.1007\/978-3-319-93000-8-94, 1803.04054","DOI":"10.1007\/978-3-319-93000-8-94"},{"key":"295_CR27","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"MN Gurcan","year":"2009","unstructured":"Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B: Histopathological image analysis: a review. IEEE Reviews in Biomedical Engineering 2: 147\u2013171, 2009. https:\/\/doi.org\/10.1109\/RBME.2009.2034865","journal-title":"IEEE Reviews in Biomedical Engineering"},{"issue":"7","key":"295_CR28","doi-asserted-by":"crossref","first-page":"e48","DOI":"10.5858\/134.7.e48","volume":"134","author":"MEH Hammond","year":"2010","unstructured":"Hammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, Fitzgibbons PL, Francis G, Goldstein NS, Hayes 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 (unabridged version). Archives of pathology & laboratory medicine 134 (7): e48\u2013e72, 2010","journal-title":"Archives of pathology & laboratory medicine"},{"key":"295_CR29","doi-asserted-by":"publisher","unstructured":"Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. Scientific Reports 7(1). https:\/\/doi.org\/10.1038\/s41598-017-04075-z","DOI":"10.1038\/s41598-017-04075-z"},{"key":"295_CR30","unstructured":"Harris J, Lippman M, Morrow M, Kent osborne C (2014) Diseases of the breast, 5th edition"},{"issue":"2","key":"295_CR31","doi-asserted-by":"publisher","first-page":"01","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin M, Sulaiman M: A review on evaluation metrics for data classification evaluations. International Journal of Data Mining &, Knowledge Management Process 5 (2): 01\u201311, 2015. https:\/\/doi.org\/10.5121\/ijdkp.2015.5201","journal-title":"International Journal of Data Mining &, Knowledge Management Process"},{"issue":"7-8","key":"295_CR32","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1016\/j.compmedimag.2010.11.009","volume":"35","author":"CH Huang","year":"2011","unstructured":"Huang CH, Veillard A, Roux L, Lom\u0117nie N, Racoceanu D: Time-efficient sparse analysis of histopathological whole slide images. Computerized Medical Imaging and Graphics 35 (7-8): 579\u2013591, 2011. https:\/\/doi.org\/10.1016\/j.compmedimag.2010.11.009","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"295_CR33","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/RBME.2013.2295804","volume":"7","author":"H Irshad","year":"2014","unstructured":"Irshad H, Veillard A, Roux L, Racoceanu D: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Reviews in Biomedical Engineering 7: 97\u2013114, 2014. https:\/\/doi.org\/10.1109\/RBME.2013.2295804","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"295_CR34","doi-asserted-by":"publisher","unstructured":"Jannesari M, Habibzadeh M, Aboulkheyr H, Khosravi P, Elemento O, Totonchi M, Hajirasouliha I (2019) Breast cancer histopathological image classification: a deep learning approach. In: Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, pp 2405\u20132412. https:\/\/doi.org\/10.1109\/BIBM.2018.8621307","DOI":"10.1109\/BIBM.2018.8621307"},{"key":"295_CR35","doi-asserted-by":"publisher","unstructured":"Jiang Y, Chen L, Zhang H, Xiao X (2019) Breast cancer histopathological image classification using convolutional neural networks with small SE-resnet module. PLoS ONE 14(3). https:\/\/doi.org\/10.1371\/journal.pone.0214587","DOI":"10.1371\/journal.pone.0214587"},{"key":"295_CR36","unstructured":"Jimenez-deltaro O, Otlora S, Andersson M, Eur\u00e9n K, Hedlund M, Rousson M, M\u00fcller H, Atzori M (2018) Analysis of histopathology images: From traditional machine learning to deep learning. In: Biomedical Texture Analysis, Elsevier, pp 281\u2013314"},{"key":"295_CR37","unstructured":"Jovanovic J (2016) Classification. http:\/\/ai.fon.bg.ac.rs\/wp-content\/uploads\/2016\/10\/Classification-basic-concepts.pdf"},{"key":"295_CR38","unstructured":"K\u00e5rsn\u00e4s A (2014) Image analysis methods and tools for digital histopathology applications relevant to breast cancer diagnosis. PhD thesis"},{"issue":"6","key":"295_CR39","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1109\/TBME.2014.2303294","volume":"61","author":"AM Khan","year":"2014","unstructured":"Khan AM, Rajpoot N, Treanor D, Magee D: A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Transactions on Biomedical Engineering 61 (6): 1729\u20131738, 2014. https:\/\/doi.org\/10.1109\/TBME.2014.2303294","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"5","key":"295_CR40","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1109\/JBHI.2015.2447008","volume":"19","author":"AM Khan","year":"2015","unstructured":"Khan AM, Sirinukunwattana K, Rajpoot N: A global covariance descriptor for nuclear atypia scoring in breast histopathology images. IEEE Journal of Biomedical and Health Informatics 19 (5): 1637\u20131647, 2015. https:\/\/doi.org\/10.1109\/JBHI.2015.2447008","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"2015","key":"295_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/457906","volume":"2015","author":"R Kumar","year":"2015","unstructured":"Kumar R, Srivastava R, Srivastava S: Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. Journal of Medical Engineering 2015 (2015): 1\u201314, 2015. https:\/\/doi.org\/10.1155\/2015\/457906","journal-title":"Journal of Medical Engineering"},{"issue":"3","key":"295_CR42","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1111\/jmi.12237","volume":"258","author":"C Lu","year":"2015","unstructured":"Lu C, Ji M, Ma Z, Mandal M: Automated image analysis of nuclear atypia in high-power field histopathological image. Journal of Microscopy 258 (3): 233\u2013240, 2015. https:\/\/doi.org\/10.1111\/jmi.12237","journal-title":"Journal of Microscopy"},{"key":"295_CR43","doi-asserted-by":"publisher","unstructured":"Lyon HO, De Leenheer AP, Horobin RW, lambert WE, schulte EK, Van Liedekerke B, Wittekind DH (1994) Standardization of reagents and methods used in cytological and histological practice with emphasis on dyes, stains and chromogenic reagents. https:\/\/doi.org\/10.1007\/BF00158587","DOI":"10.1007\/BF00158587"},{"key":"295_CR44","doi-asserted-by":"publisher","unstructured":"Macenko M, Niethammer M, Marron JS, Borland D, Woosley JT, Guan X, Schmitt C, Thomas NE (2009) A method for normalizing histology slides for quantitative analysis. In: Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp 1107\u20131110. https:\/\/doi.org\/10.1109\/ISBI.2009.5193250","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"295_CR45","doi-asserted-by":"publisher","unstructured":"Malvia S, Bagadi SA, Dubey US, Saxena S Epidemiology of breast cancer in indian women 13(4), 289\u2013295, 2017. https:\/\/doi.org\/10.1111\/ajco.12661","DOI":"10.1111\/ajco.12661"},{"key":"295_CR46","doi-asserted-by":"publisher","unstructured":"Maqlin P, Thamburaj R, Mammen JJ, Manipadam MT (2015) Automated nuclear pleomorphism scoring in breast cancer histopathology images using deep neural networks. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9468, pp 269\u2013276. https:\/\/doi.org\/10.1007\/978.3.319.26832.3.26","DOI":"10.1007\/978.3.319.26832.3.26"},{"key":"295_CR47","doi-asserted-by":"publisher","unstructured":"Moncayo R, Romo-Bucheli D, Romero E (2015) A grading strategy for nuclear pleomorphism in histopathological breast cancer images using a bag of features (bof). In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9423, pp 75\u201382. https:\/\/doi.org\/10.1007\/978.3.319.25751.8.10","DOI":"10.1007\/978.3.319.25751.8.10"},{"key":"295_CR48","doi-asserted-by":"crossref","unstructured":"Nahid AA, Mehrabi MA, Kong Y (2018) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed research international 2018","DOI":"10.1155\/2018\/2362108"},{"key":"295_CR49","doi-asserted-by":"publisher","unstructured":"Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: 2008 5Th IEEE international symposium on biomedical imaging: From nano to macro, Proceedings, ISBI, pp 284\u2013287. https:\/\/doi.org\/10.1109\/ISBI.2008.4540988","DOI":"10.1109\/ISBI.2008.4540988"},{"key":"295_CR50","doi-asserted-by":"publisher","unstructured":"Nejad EM, Affendey LS, Latip RB, Bin Ishak I (2017) Classification of Histopathology Images of Breast into Benign and Malignant using a Single-layer Convolutional Neural Network. In: Proceedings of the International Conference on Imaging, Signal Processing and Communication - ICISPC, vol 2017, pp 50\u201353. https:\/\/doi.org\/10.1145\/3132300.3132331","DOI":"10.1145\/3132300.3132331"},{"key":"295_CR51","doi-asserted-by":"crossref","unstructured":"Niethammer M, Borland D, Marron J, Woosley JT, Thomas NE (2010) Appearance normalization of histology slides. In: MLMI, Springer, pp 58\u201366","DOI":"10.1007\/978-3-642-15948-0_8"},{"issue":"1","key":"295_CR52","doi-asserted-by":"publisher","first-page":"S29","DOI":"10.1186\/1746-1596-8-S1-S29","volume":"8","author":"V Ojansivu","year":"2013","unstructured":"Ojansivu V, Linder N, Rahtu E, Pietik\u00e4inen M, Lundin M, Joensuu H, Lundin J: Automated classification of breast cancer morphology in histopathological images. Diagnostic Pathology 8 (1): S29, 2013","journal-title":"Diagnostic Pathology"},{"key":"295_CR53","doi-asserted-by":"crossref","unstructured":"Petushi S, Katsinis C, Coward C, Garcia F, Tozeren A (2004) Automated identification of microstructures on histology slides. In: 2004 IEEE International Symposium on Biomedical imaging: Nano to macro, IEEE, pp 424\u2013427","DOI":"10.1109\/ISBI.2004.1398565"},{"issue":"1","key":"295_CR54","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/1471-2342-6-14","volume":"6","author":"S Petushi","year":"2006","unstructured":"Petushi S, Garcia FU, Haber MM, Katsinis C, Tozeren A: Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Medical Imaging 6 (1): 14, 2006. https:\/\/doi.org\/10.1186\/1471.2342.6.14","journal-title":"BMC Medical Imaging"},{"key":"295_CR55","doi-asserted-by":"publisher","unstructured":"Rakhlin A, Shvets A, Iglovikov V, Kalinin AA (2018) Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 10882 LNCS, pp 737\u2013744. https:\/\/doi.org\/10.1007\/978-3-319-93000-8-83, 1802.00752","DOI":"10.1007\/978-3-319-93000-8-83"},{"issue":"4","key":"295_CR56","doi-asserted-by":"publisher","first-page":"044,501","DOI":"10.1117\/1.JMI.3.4.044501","volume":"3","author":"H Rezaeilouyeh","year":"2016","unstructured":"Rezaeilouyeh H, Mollahosseini A, Mohammad MH: Microscopic medical image classification framework via deep learning and shearlet transform. Journal of Medical Imaging 3 (4): 044,501, 2016. https:\/\/doi.org\/10.1117\/1.JMI.3.4.044501","journal-title":"Journal of Medical Imaging"},{"key":"295_CR57","unstructured":"Rolls G (2010) Microtomy and Paraffin Section Preparation. Scientia Leica Microsystems\u2019 Education Series, pp 32, https:\/\/www.leica-microsystems.com"},{"issue":"1","key":"295_CR58","doi-asserted-by":"publisher","first-page":"8","DOI":"10.4103\/2153-3539.112693","volume":"4","author":"L Roux","year":"2013","unstructured":"Roux L, Racoceanu D, Lom\u0117nie N, Kulikova M, Irshad H, Klossa J, Capron F, Genestie C, Naour G, Gurcan M: Mitosis detection in breast cancer histological images an ICPR 2012 contest. Journal of Pathology Informatics 4 (1): 8, 2013. https:\/\/doi.org\/10.4103\/2153-3539.112693","journal-title":"Journal of Pathology Informatics"},{"issue":"4","key":"295_CR59","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1097\/00129039-200303000-00014","volume":"23","author":"AC Ruifrok","year":"2001","unstructured":"Ruifrok AC, Johnston DA: Quantification of histochemical staining by color deconvolution. Analytical and Quantitative Cytology and Histology 23 (4): 291\u2013299, 2001. https:\/\/doi.org\/10.1097\/00129039-200303000-00014, arXiv:1011.1669v3","journal-title":"Analytical and Quantitative Cytology and Histology"},{"key":"295_CR60","doi-asserted-by":"publisher","unstructured":"Salahuddin T, Haouari F, Islam F, Ali R, Al-Rasbi S, Aboueata N, Rezk E, Jaoua A Breast cancer image classification using pattern-based Hyper Conceptual Sampling method. Informatics in Medicine Unlocked, pp 1\u201310, 2018. https:\/\/doi.org\/10.1016\/j.imu.2018.07.002","DOI":"10.1016\/j.imu.2018.07.002"},{"issue":"7","key":"295_CR61","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","volume":"63","author":"FA Spanhol","year":"2016","unstructured":"Spanhol FA, Oliveira LS, Petitjean C, Heutte L: A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering 63 (7): 1455\u20131462, 2016. https:\/\/doi.org\/10.1109\/TBME.2015.2496264","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"295_CR62","doi-asserted-by":"crossref","unstructured":"Spanhol FA, Oliveira LS, Cavalin PR, Petitjean C, Heutte L (2017) Deep features for breast cancer histopathological image classification. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, pp 1868\u2013 1873","DOI":"10.1109\/SMC.2017.8122889"},{"issue":"2","key":"295_CR63","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/BF01834640","volume":"20","author":"M Stierer","year":"1991","unstructured":"Stierer M, Rosen H, Weber R: Nuclear pleomorphism, a strong prognostic factor in axillary node-negative small invasive breast cancer. Breast Cancer Research and Treatment 20 (2): 109\u2013116, 1991. https:\/\/doi.org\/10.1007\/BF01834640","journal-title":"Breast Cancer Research and Treatment"},{"issue":"3","key":"295_CR64","doi-asserted-by":"publisher","first-page":"199","DOI":"10.2350\/06-06-0121.1","volume":"10","author":"LA Teot","year":"2007","unstructured":"Teot LA, Sposto R, Khayat A, Qualman S, Reaman G, Parham D: The problems and promise of central pathology review: development of a standardized procedure for the Children\u2019s Oncology Group. Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society 10 (3): 199\u2013207, 2007. https:\/\/doi.org\/10.2350\/06-06-0121.1","journal-title":"Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society"},{"key":"295_CR65","doi-asserted-by":"crossref","unstructured":"Vang YS, Chen Z, Xie X (2018) Deep learning framework for multi-class breast cancer histology image classification. In: International Conference Image Analysis and Recognition, Springer, pp 914\u2013922","DOI":"10.1007\/978-3-319-93000-8_104"},{"key":"295_CR66","doi-asserted-by":"publisher","unstructured":"Vesal S, Ravikumar N, Davari AA, Ellmann S, Maier A (2018) Classification of Breast Cancer Histology Images Using Transfer Learning. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 10882 LNCS, pp 812\u2013819. https:\/\/doi.org\/10.1007\/978-3-319-93000-8-92, 1802.09424","DOI":"10.1007\/978-3-319-93000-8-92"},{"issue":"12","key":"295_CR67","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/modpathol.2012.126","volume":"25","author":"M Veta","year":"2012","unstructured":"Veta M, Kornegoor R, Huisman A, Verschuur-Maes AHJ, Viergever MA, Pluim JPW, van Diest PJ: Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer. Modern Pathology 25 (12): 1559\u20131565, 2012. https:\/\/doi.org\/10.1038\/modpathol.2012.126","journal-title":"Modern Pathology"},{"issue":"1","key":"295_CR68","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.media.2014.11.010","volume":"20","author":"M Veta","year":"2015","unstructured":"Veta M, van Diest PJ, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, Gonzalez F, Larsen AB, Vestergaard JS, Dahl AB, Cirean DC, Schmidhuber J, Giusti A, Gambardella LM, Tek FB, Walter T, Wang CW, Kondo S, Matuszewski BJ, Precioso F, Snell V, Kittler J, de Campos TE, Khan AM, Rajpoot NM, Arkoumani E, Lacle MM, Viergever MA, Pluim JP: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Analysis 20 (1): 237\u2013248, 2015. https:\/\/doi.org\/10.1016\/j.media.2014.11.010, 1411.5825","journal-title":"Medical Image Analysis"},{"key":"295_CR69","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2016.05.084","volume":"229","author":"T Wan","year":"2017","unstructured":"Wan T, Cao J, Chen J, Qin Z: Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 229: 34\u201344, 2017. https:\/\/doi.org\/10.1016\/j.neucom.2016.05.084","journal-title":"Neurocomputing"},{"key":"295_CR70","doi-asserted-by":"publisher","unstructured":"Wei B, Han Z, He X, Yin Y (2017) Deep learning model based breast cancer histopathological image classification. In: 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp 348\u2013353. https:\/\/doi.org\/10.1109\/ICCCBDA.2017.7951937","DOI":"10.1109\/ICCCBDA.2017"},{"key":"295_CR71","unstructured":"Wetzel AW, John RGI, Beckstead JA, Feineigle PA, Hauser CR, Palmieri FA Jr (2006) System for creating microscopic digital montage images. US Patent 7,155,049"},{"key":"295_CR72","doi-asserted-by":"publisher","unstructured":"Weyn B, Van De Wouwer G, Van Daele A, Scheunders P, Van Dyck D, Van Marck E, Jacob W: Automated breast tumor diagnosis and grading based on wavelet chromatin texture description. Cytometry 33 (1): 32\u201340, 1998. https:\/\/doi.org\/10.1002\/(SICI)1097-0320(19980901)33:1.32::AID-CYTO4.3.0.CO;2-D","DOI":"10.1002\/(SICI)1097-0320(19980901)33:1.32::AID-CYTO4.3.0.CO;2-D"},{"issue":"4","key":"295_CR73","first-page":"257","volume":"17","author":"WH Wolberg","year":"1995","unstructured":"Wolberg WH, Street WN, Heisey DM, Mangasarian OL: Computer-derived nuclear \u201dgrade\u201d and breast cancer prognosis. Analytical and Quantitative Cytology and Histology 17 (4): 257\u201364, 1995","journal-title":"Analytical and Quantitative Cytology and Histology"},{"issue":"1","key":"295_CR74","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1200\/JCO.2006.09.2775","volume":"25","author":"AC Wolff","year":"2006","unstructured":"Wolff AC, Hammond MEH, Schwartz JN, Hagerty KL, Allred DC, Cote RJ, Dowsett M, Fitzgibbons PL, Hanna WM, Langer A, et al.: American society of clinical oncology\/college of american pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. Journal of Clinical Oncology 25 (1): 118\u2013145, 2006","journal-title":"Journal of Clinical Oncology"},{"key":"295_CR75","unstructured":"Wollmann T, Rohr K (2017) Automatic breast cancer grading in lymph nodes using a deep neural network. arXiv:170707565"},{"key":"295_CR76","doi-asserted-by":"publisher","unstructured":"Xu J, Zhou C, Lang B, Liu Q (2017) Deep learning for histopathological image analysis: Towards computerized diagnosis on cancers. In: Advances in Computer Vision and Pattern Recognition, pp 73\u201395, https:\/\/doi.org\/10.1007\/978-3-319-42999-1-6","DOI":"10.1007\/978-3-319-42999-1-6"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00295-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-019-00295-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00295-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T07:29:52Z","timestamp":1614151792000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-019-00295-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,27]]},"references-count":76,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["295"],"URL":"https:\/\/doi.org\/10.1007\/s10278-019-00295-z","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,27]]},"assertion":[{"value":"27 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}