{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:47:00Z","timestamp":1772549220380,"version":"3.50.1"},"reference-count":183,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T00:00:00Z","timestamp":1558742400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T00:00:00Z","timestamp":1558742400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"RG380-17AFR","award":["RG380-17AFR"],"award-info":[{"award-number":["RG380-17AFR"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1007\/s10462-019-09716-5","type":"journal-article","created":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T12:02:32Z","timestamp":1558785752000},"page":"1655-1720","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":266,"title":["Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges"],"prefix":"10.1007","volume":"53","author":[{"given":"Ghulam","family":"Murtaza","sequence":"first","affiliation":[]},{"given":"Liyana","family":"Shuib","sequence":"additional","affiliation":[]},{"given":"Ainuddin Wahid","family":"Abdul Wahab","sequence":"additional","affiliation":[]},{"given":"Ghulam","family":"Mujtaba","sequence":"additional","affiliation":[]},{"given":"Ghulam","family":"Mujtaba","sequence":"additional","affiliation":[]},{"given":"Henry Friday","family":"Nweke","sequence":"additional","affiliation":[]},{"given":"Mohammed Ali","family":"Al-garadi","sequence":"additional","affiliation":[]},{"given":"Fariha","family":"Zulfiqar","sequence":"additional","affiliation":[]},{"given":"Ghulam","family":"Raza","sequence":"additional","affiliation":[]},{"given":"Nor Aniza","family":"Azmi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,25]]},"reference":[{"key":"9716_CR1","first-page":"901","volume-title":"Handbook of measuring system design","author":"A Abraham","year":"2005","unstructured":"Abraham A (2005) Artificial\nneural networks. In: Peter H,\nSydenham RT\n(eds) Handbook of measuring\nsystem design. Wiley, London, pp\n901\u2013908"},{"key":"9716_CR2","doi-asserted-by":"crossref","unstructured":"Adoui ME, Drisis S, Benjelloun M (2017) Analyzing breast tumor heterogeneity to predict the response to chemotherapy using 3D MR images registration. Paper presented at the Proceedings of the 2017 international conference on smart digital environment, Rabat, Morocco. \nhttp:\/\/delivery.acm.org\/10.1145\/3130000\/3128137\/p56-el_adoui.pdf?ip=103.18.2.251&id=3128137&acc=ACTIVE%20SERVICE&key=69AF3716A20387ED%2EE7759EC8BE158239%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1527212708_ca160ac108047af6b06969e33a701e4f\n\n. Accessed 15 Aug 2018","DOI":"10.1145\/3128128.3128137"},{"key":"9716_CR3","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1186\/1477-7819-11-130","volume":"11","author":"SJ Ahn","year":"2013","unstructured":"Ahn SJ, Kim YS, Kim EY, Park HK, Cho EK, Kim YK et al (2013) The value of chest CT for prediction of breast tumor size: comparison with pathology measurement. World J Surg Oncol 11:130. \nhttps:\/\/doi.org\/10.1186\/1477-7819-11-130","journal-title":"World J Surg Oncol"},{"key":"9716_CR4","doi-asserted-by":"crossref","unstructured":"Aksebzeci BH, Kayaalti \u00d6 (2017) Computer-aided classification of breast cancer histopathological images. Paper presented at the 2017 Medical Technologies National Congress (TIPTEKNO)","DOI":"10.1109\/TIPTEKNO.2017.8238076"},{"key":"9716_CR5","doi-asserted-by":"crossref","unstructured":"Amit G, Ben-Ari R, Hadad O, Monovich E, Granot N, Hashoul S (2017) Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches. Paper presented at the progress in biomedical optics and imaging\u2014proceedings of SPIE","DOI":"10.1117\/12.2249981"},{"key":"9716_CR6","unstructured":"Amodei D, Ananthanarayanan S, Anubhai R, Bai J, Battenberg E, Case C et al (2016) Deep speech 2: end-to-end speech recognition in English and mandarin. Paper presented at the International conference on machine learning"},{"issue":"1","key":"9716_CR7","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1117\/1.jmi.5.1.014503","volume":"5","author":"N Antropova","year":"2018","unstructured":"Antropova N, Abe H, Giger ML (2018a) Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks. J Med Imaging 5(1):6. \nhttps:\/\/doi.org\/10.1117\/1.jmi.5.1.014503","journal-title":"J Med Imaging"},{"key":"9716_CR8","unstructured":"Antropova N, Huynh B, Giger M (2018) Recurrent neural networks for breast lesion classification based on DCE-MRIs. Paper presented at the progress in biomedical optics and imaging\u2014proceedings of SPIE"},{"issue":"6","key":"9716_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1371\/journal.pone.0177544","volume":"12","author":"T Araujo","year":"2017","unstructured":"Araujo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C et al (2017) Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6):14. \nhttps:\/\/doi.org\/10.1371\/journal.pone.0177544","journal-title":"PLoS ONE"},{"issue":"6","key":"9716_CR10","doi-asserted-by":"publisher","first-page":"e0177544","DOI":"10.1371\/journal.pone.0177544","volume":"12","author":"T Ara\u00fajo","year":"2017","unstructured":"Ara\u00fajo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C et al (2017) Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6):e0177544","journal-title":"PLoS ONE"},{"key":"9716_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1748-0221\/10\/12\/t12002","author":"D Arefan","year":"2015","unstructured":"Arefan D, Talebpour A, Ahmadinejhad N, Asl AK (2015) Automatic breast density classification using neural network. J Instrum. \nhttps:\/\/doi.org\/10.1088\/1748-0221\/10\/12\/t12002","journal-title":"J Instrum"},{"key":"9716_CR12","doi-asserted-by":"crossref","unstructured":"Arevalo J, Gonz\u00e1lez FA, Ramos-Poll\u00e1n R, Oliveira JL, Lopez MAG (2015) Convolutional neural networks for mammography mass lesion classification. Paper presented at the 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","DOI":"10.1109\/EMBC.2015.7318482"},{"key":"9716_CR13","doi-asserted-by":"crossref","unstructured":"Bakkouri I, Afdel K (2017) Breast tumor classification based on deep convolutional neural networks. Paper presented at the Proceedings\u20143rd international conference on advanced technologies for signal and image processing, ATSIP 2017","DOI":"10.1109\/ATSIP.2017.8075562"},{"key":"9716_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2831280","author":"D Bardou","year":"2018","unstructured":"Bardou D, Zhang K, Ahmad SM (2018) Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access. \nhttps:\/\/doi.org\/10.1109\/access.2018.2831280","journal-title":"IEEE Access"},{"issue":"5","key":"9716_CR15","doi-asserted-by":"publisher","first-page":"773","DOI":"10.7863\/jum.2012.31.5.773","volume":"31","author":"RG Barr","year":"2012","unstructured":"Barr RG (2012) Sonographic breast elastography: a primer. J Ultrasound Med 31(5):773\u2013783","journal-title":"J Ultrasound Med"},{"key":"9716_CR16","doi-asserted-by":"crossref","unstructured":"Bayramoglu N, Kannala J, Heikkila J (2017) Deep learning for magnification independent breast cancer histopathology image classification. Paper presented at the Proceedings\u2014international conference on pattern recognition","DOI":"10.1109\/ICPR.2016.7900002"},{"issue":"4","key":"9716_CR17","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/45.329294","volume":"13","author":"G Bebis","year":"1994","unstructured":"Bebis G, Georgiopoulos M (1994) Feed-forward neural networks. IEEE Potentials 13(4):27\u201331","journal-title":"IEEE Potentials"},{"key":"9716_CR18","unstructured":"Bejnordi BE, Lin J, Glass B, Mullooly M, Gierach GL, Sherman ME et al (2017a) Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. Paper presented at the 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017)"},{"issue":"4","key":"9716_CR19","doi-asserted-by":"publisher","first-page":"8","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 et al (2017b) Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. J Med Imaging 4(4):8. \nhttps:\/\/doi.org\/10.1117\/1.jmi.4.4.044504","journal-title":"J Med Imaging"},{"key":"9716_CR20","doi-asserted-by":"crossref","unstructured":"Bekker AJ, Greenspan H, Goldberger J (2016) A multi-view deep learning architecture for classification of breast microcalcifications. Paper presented at the Proceedings\u2014international symposium on biomedical imaging","DOI":"10.1109\/ISBI.2016.7493369"},{"issue":"1","key":"9716_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio Y (2009) Learning deep architectures for AI. Found Trends\u00ae Mach Learn 2(1):1\u2013127. \nhttps:\/\/doi.org\/10.1561\/2200000006","journal-title":"Found Trends\u00ae Mach Learn"},{"key":"9716_CR22","volume-title":"Handbook of medical imaging","author":"J Beutel","year":"2000","unstructured":"Beutel J, Kundel HL, Van Metter RL (2000) Handbook of medical imaging, vol 1. SPIE Press, Bellingham"},{"key":"9716_CR23","doi-asserted-by":"crossref","unstructured":"Bevilacqua V, Brunetti A, Triggiani M, Magaletti D, Telegrafo M, Moschetta M (2016) An optimized feed-forward artificial neural network topology to support radiologists in breast lesions classification. Paper presented at the GECCO 2016 companion\u2014proceedings of the 2016 genetic and evolutionary computation conference","DOI":"10.1145\/2908961.2931733"},{"key":"9716_CR24","unstructured":"Breast Cancer Imaging (2018) Breast Cancer Imaging. Retrieved from \nhttp:\/\/www.aboutcancer.com\/breast_cancer_imaging.htm\n\n. Accessed 20 Aug 2018"},{"key":"9716_CR25","doi-asserted-by":"crossref","unstructured":"Byra M, Piotrzkowska-Wroblewska H, Dobruch-Sobczak K, Nowicki A (2017) Combining Nakagami imaging and convolutional neural network for breast lesion classification. Paper presented at the IEEE international ultrasonics symposium, IUS","DOI":"10.1109\/ULTSYM.2017.8092154"},{"key":"9716_CR26","doi-asserted-by":"crossref","unstructured":"Cao J, Qin Z, Jing J, Chen J, Wan T (2016) An automatic breast cancer grading method in histopathological images based on pixel-, object-, and semantic-level features. Paper presented at the 2016 IEEE 13th international symposium on biomedical imaging (ISBI)","DOI":"10.1109\/ISBI.2016.7493470"},{"key":"9716_CR27","doi-asserted-by":"crossref","unstructured":"Carneiro G, Nascimento J,\nBradley AP (2015) Unregistered\nmultiview mammogram\nanalysis with pre-trained deep\nlearning models. In:\nInternational conference on\nmedical image computing\nand computer-assisted\nintervention.\nSpringer, Cham, pp. 652\u2013660","DOI":"10.1007\/978-3-319-24574-4_78"},{"issue":"11","key":"9716_CR28","doi-asserted-by":"publisher","first-page":"2355","DOI":"10.1109\/TMI.2017.2751523","volume":"36","author":"G Carneiro","year":"2017","unstructured":"Carneiro G, Nascimento J, Bradley AP (2017) Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imaging 36(11):2355\u20132365. \nhttps:\/\/doi.org\/10.1109\/TMI.2017.2751523","journal-title":"IEEE Trans Med Imaging"},{"issue":"12","key":"9716_CR29","doi-asserted-by":"publisher","first-page":"5017","DOI":"10.1109\/TIP.2015.2475625","volume":"24","author":"T Chan","year":"2015","unstructured":"Chan T, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24(12):5017\u20135032. \nhttps:\/\/doi.org\/10.1109\/TIP.2015.2475625","journal-title":"IEEE Trans Image Process"},{"key":"9716_CR30","doi-asserted-by":"crossref","unstructured":"Chang J, Yu J, Han T, Chang H, Park E (2017) A method for classifying medical images using transfer learning: a pilot study on histopathology of breast cancer. Paper presented at the 2017 IEEE 19th international conference on e-health networking, applications and services (Healthcom)","DOI":"10.1109\/HealthCom.2017.8210843"},{"key":"9716_CR31","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.media.2016.11.004","volume":"36","author":"H Chen","year":"2017","unstructured":"Chen H, Qi X, Yu L, Dou Q, Qin J, Heng P-A (2017a) DCAN: deep contour-aware networks for object instance segmentation from histology images. Med Image Anal 36:135\u2013146. \nhttps:\/\/doi.org\/10.1016\/j.media.2016.11.004","journal-title":"Med Image Anal"},{"issue":"3","key":"9716_CR32","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1177\/1010428317694550","volume":"39","author":"JM Chen","year":"2017","unstructured":"Chen JM, Li Y, Xu J, Gong L, Wang LW, Liu WL, Liu J (2017b) Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: a review. Tumor Biol 39(3):12. \nhttps:\/\/doi.org\/10.1177\/1010428317694550","journal-title":"Tumor Biol"},{"key":"9716_CR33","doi-asserted-by":"publisher","first-page":"24454","DOI":"10.1038\/srep24454","volume":"6","author":"J-Z Cheng","year":"2016","unstructured":"Cheng J-Z, Ni D, Chou Y-H, Qin J, Tiu C-M, Chang Y-C et al (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 6:24454. \nhttps:\/\/doi.org\/10.1038\/srep24454","journal-title":"Sci Rep"},{"key":"9716_CR34","unstructured":"Chris Rose DT, Williams A, Wolstencroft K, Taylor C (2006) DDSM: digital database for screening mammography. Retrieved from \nhttp:\/\/marathon.csee.usf.edu\/Mammography\/Database.html\n\n. Accessed 26 Aug 2018"},{"issue":"6","key":"9716_CR35","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P et al (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045\u20131057. \nhttps:\/\/doi.org\/10.1007\/s10278-013-9622-7","journal-title":"J Digit Imaging"},{"key":"9716_CR36","doi-asserted-by":"crossref","unstructured":"Concei\u00e7\u00e3o RC, Medeiros H, Halloran MO, Rodriguez-Herrera D, Flores-Tapia D, Pistorius S (2014) SVM-based classification of breast tumour phantoms using a UWB radar prototype system. Paper presented at the 2014 XXXIth URSI general assembly and scientific symposium (URSI GASS)","DOI":"10.1109\/URSIGASS.2014.6930131"},{"key":"9716_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/srep46450","author":"A Cruz-Roa","year":"2017","unstructured":"Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC et al (2017) Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci Rep. \nhttps:\/\/doi.org\/10.1038\/srep46450","journal-title":"Sci Rep"},{"issue":"3","key":"9716_CR38","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1287\/mnsc.9.3.458","volume":"9","author":"N Dalkey","year":"1963","unstructured":"Dalkey N, Helmer O (1963) An experimental application of the Delphi method to the use of experts. Manag Sci 9(3):458\u2013467","journal-title":"Manag Sci"},{"key":"9716_CR39","doi-asserted-by":"crossref","unstructured":"De S Silva SD, Costa MGF, De A Pereira WC, Filho CFFC (2015) Breast tumor classification in ultrasound images using neural networks with improved generalization methods. Paper presented at the Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBS","DOI":"10.1109\/EMBC.2015.7319838"},{"issue":"3\u20134","key":"9716_CR40","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1561\/2000000039","volume":"7","author":"L Deng","year":"2014","unstructured":"Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends\u00ae Signal Process 7(3\u20134):197\u2013387. \nhttps:\/\/doi.org\/10.1561\/2000000039","journal-title":"Found Trends\u00ae Signal Process"},{"key":"9716_CR41","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.media.2017.01.009","volume":"37","author":"N Dhungel","year":"2017","unstructured":"Dhungel N, Carneiro G, Bradley AP (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 37:114\u2013128. \nhttps:\/\/doi.org\/10.1016\/j.media.2017.01.009","journal-title":"Med Image Anal"},{"issue":"4\u20135","key":"9716_CR42","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","volume":"31","author":"K Doi","year":"2007","unstructured":"Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph\u00a031(4\u20135):198\u2013211","journal-title":"Comput Med Imaging Graph"},{"key":"9716_CR43","unstructured":"Dua D (2017) KT UCI machine learning repository. University of California, School of Information and Computer Science, Irvine. Retrieved from \nhttp:\/\/archive.ics.uci.edu\/ml"},{"issue":"1","key":"9716_CR44","first-page":"163","volume":"2","author":"W Duch","year":"1999","unstructured":"Duch W, Jankowski N (1999) Survey of neural transfer functions. Neural Comput Surv 2(1):163\u2013212","journal-title":"Neural Comput Surv"},{"issue":"8","key":"9716_CR45","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1049\/iet-cvi.2016.0425","volume":"11","author":"S Duraisamy","year":"2017","unstructured":"Duraisamy S, Emperumal S (2017) Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier. IET Comput Vision 11(8):656\u2013662. \nhttps:\/\/doi.org\/10.1049\/iet-cvi.2016.0425","journal-title":"IET Comput Vision"},{"issue":"3","key":"9716_CR46","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1148\/radiol.2533082308","volume":"253","author":"JG Elmore","year":"2009","unstructured":"Elmore JG, Jackson SL, Abraham L, Miglioretti DL, Carney PA, Geller BM et al (2009) Variability in interpretive performance at screening mammography and radiologists\u2019 characteristics associated with accuracy. Radiology 253(3):641\u2013651","journal-title":"Radiology"},{"key":"9716_CR47","doi-asserted-by":"crossref","unstructured":"Ertosun MG, Rubin DL (2015) Probabilistic visual search for masses within mammography images using deep learning. Paper presented at the 2015 IEEE international conference on bioinformatics and biomedicine (BIBM)","DOI":"10.1109\/BIBM.2015.7359868"},{"key":"9716_CR48","first-page":"23","volume":"7","author":"N Farahani","year":"2015","unstructured":"Farahani N, Parwani AV, Pantanowitz L (2015) Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol Lab Med Int 7:23\u201333","journal-title":"Pathol Lab Med Int"},{"issue":"2","key":"9716_CR49","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s11548-017-1663-9","volume":"13","author":"Y Feng","year":"2018","unstructured":"Feng Y, Zhang L, Yi Z (2018) Breast cancer cell nuclei classification in histopathology images using deep neural networks. Int J Comput Assist Radiol Surg 13(2):179\u2013191. \nhttps:\/\/doi.org\/10.1007\/s11548-017-1663-9","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"9716_CR50","doi-asserted-by":"crossref","unstructured":"Ferri C, Hern\u00e1ndez-Orallo J, Salido MA (2003) Volume under the ROC surface for multi-class problems. Paper presented at the European conference on machine learning","DOI":"10.1007\/978-3-540-39857-8_12"},{"key":"9716_CR51","doi-asserted-by":"crossref","unstructured":"Fischer A, Igel C (2012) An introduction to restricted Boltzmann machines. Paper presented at the Iberoamerican Congress on pattern recognition","DOI":"10.1007\/978-3-642-33275-3_2"},{"key":"9716_CR52","doi-asserted-by":"crossref","unstructured":"Fonseca P, Mendoza J, Wainer J, Ferrer J, Pinto J, Guerrero J, Castaneda B (2015) Automatic breast density classification using a convolutional neural network architecture search procedure. Paper presented at the Progress in biomedical optics and imaging\u2014proceedings of SPIE","DOI":"10.1117\/12.2081576"},{"key":"9716_CR53","first-page":"267","volume-title":"Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. Competition and cooperation in neural nets","author":"K Fukushima","year":"1982","unstructured":"Fukushima K, Miyake S (1982) Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. Competition and cooperation in neural nets. Springer, Berlin, pp 267\u2013285"},{"key":"9716_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.04.005","author":"Z Gandomkar","year":"2018","unstructured":"Gandomkar Z, Brennan PC, Mello-Thoms C (2018) MuDeRN: multi-category classification of breast histopathological image using deep residual networks. Artif Intell Med. \nhttps:\/\/doi.org\/10.1016\/j.artmed.2018.04.005","journal-title":"Artif Intell Med"},{"key":"9716_CR55","doi-asserted-by":"crossref","unstructured":"Gardezi SJS, Awais M, Faye I, Meriaudeau F (2017) Mammogram classification using deep learning features. Paper presented at the 2017 IEEE international conference on signal and image processing applications (ICSIPA)","DOI":"10.1109\/ICSIPA.2017.8120660"},{"key":"9716_CR56","unstructured":"Goceri E (2017) Advances\nin digital pathology. Paper\npresented at the international\nconference on applied\nanalysis and mathematical\nmodeling. Istanbul, Turkey"},{"key":"9716_CR57","unstructured":"Goceri E (2018) Formulas\nbehind deep learning\nsuccess. Paper presented at\nthe international conference\non applied analysis and\nmathematical modeling.\nIstanbul, Turkey"},{"key":"9716_CR58","unstructured":"Goceri E, Goceri N\n(2017) Deep learning in\nmedical image analysis:\nrecent advances and future\ntrends. Paper presented at the\ninternational conferences\ncomputer graphics,\nvisualization, computer\nvision and image processing.\nIstanbul, Turkey"},{"key":"9716_CR59","unstructured":"Goceri E, Gooya A\n(2018) On the importance of\nbatch size for deep learning.\nPaper presented at the\ninternational conference on\nmathematics. Istanbul,\nTurkey"},{"key":"9716_CR60","unstructured":"Goceri E, Songul C\n(2018) Biomedical\ninformation technology:\nimage based computer aided\ndiagnosis systems. Paper presented at the international\nconference on advanced\ntechnologies. Antalya,\nTurkey"},{"key":"9716_CR61","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","volume":"2","author":"MN Gurcan","year":"2009","unstructured":"Gurcan MN, Boucheron L, Can A, Madabhushi A, Rajpoot N, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147","journal-title":"IEEE Rev Biomed Eng"},{"key":"9716_CR62","doi-asserted-by":"crossref","unstructured":"Haarburger C, Langenberg P, Truhn D, Schneider H, Th\u00fcring J, Schrading S et al (2018) Transfer learning for breast cancer malignancy classification based on dynamic contrast-enhanced MR images. Paper presented at the Informatik aktuell","DOI":"10.1007\/978-3-662-56537-7_61"},{"key":"9716_CR63","doi-asserted-by":"crossref","unstructured":"Hadad O, Bakalo R, Ben-Ari R, Hashoul S, Amit G (2017) Classification of breast lesions using cross-modal deep learning. Paper presented at the proceedings\u2014international symposium on biomedical imaging","DOI":"10.1109\/ISBI.2017.7950480"},{"issue":"19","key":"9716_CR64","doi-asserted-by":"publisher","first-page":"7714","DOI":"10.1088\/1361-6560\/aa82ec","volume":"62","author":"S Han","year":"2017","unstructured":"Han S, Kang HK, Jeong JY, Park MH, Kim W, Bang WC, Seong YK (2017a) A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol 62(19):7714\u20137728. \nhttps:\/\/doi.org\/10.1088\/1361-6560\/aa82ec","journal-title":"Phys Med Biol"},{"key":"9716_CR65","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-04075-z","author":"Z Han","year":"2017","unstructured":"Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017b) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep. \nhttps:\/\/doi.org\/10.1038\/s41598-017-04075-z","journal-title":"Sci Rep"},{"key":"9716_CR66","unstructured":"Hannun A, Case C, Casper J, Catanzaro B, Diamos G, Elsen E et al (2014) Deep speech: scaling up end-to-end speech recognition. arXiv preprint \narXiv:1412.5567"},{"key":"9716_CR67","doi-asserted-by":"crossref","unstructured":"Hassan NA, Yassin AH, Tayel MB, Mohamed MM (2016) Ultra-wideband scattered microwave signals for detection of breast tumors using artifical neural networks. Paper presented at the 2016 3rd International conference on artificial intelligence and pattern recognition, AIPR 2016","DOI":"10.1109\/ICAIPR.2016.7585226"},{"issue":"5","key":"9716_CR68","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TMI.2007.908687","volume":"27","author":"X He","year":"2008","unstructured":"He X, Frey EC (2008) The meaning and use of the volume under a three-class ROC surface (VUS). IEEE Trans Med Imaging 27(5):577\u2013588","journal-title":"IEEE Trans Med Imaging"},{"key":"9716_CR69","unstructured":"Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2000) The digital database for screening mammography. Paper presented at the Proceedings of the 5th international workshop on digital mammography"},{"issue":"5786","key":"9716_CR70","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"issue":"7","key":"9716_CR71","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"issue":"3","key":"9716_CR72","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1148\/radiol.2018171361","volume":"287","author":"S Hofvind","year":"2018","unstructured":"Hofvind S, Hovda T, Holen \u00c5S, Lee CI, Albertsen J, Bj\u00f8rndal H et al (2018) Digital breast tomosynthesis and synthetic 2D mammography versus digital mammography: evaluation in a population-based screening program. Radiology 287(3):787\u2013794","journal-title":"Radiology"},{"key":"9716_CR73","doi-asserted-by":"crossref","unstructured":"Hussain A, Farooq K, Luo B, Slack W (2015). A novel ontology and machine learning inspired hybrid cardiovascular decision support framework. Paper presented at the 2015 IEEE symposium series on computational intelligence","DOI":"10.1109\/SSCI.2015.122"},{"issue":"8","key":"9716_CR74","doi-asserted-by":"publisher","first-page":"138","DOI":"10.3390\/sym9080138","volume":"9","author":"KT Islam","year":"2017","unstructured":"Islam KT, Raj RG, Mujtaba G (2017) Recognition of traffic sign based on bag-of-words and artificial neural network. Symmetry 9(8):138","journal-title":"Symmetry"},{"issue":"7","key":"9716_CR75","first-page":"286","volume":"8","author":"MA Jaffar","year":"2017","unstructured":"Jaffar MA (2017) Deep learning based computer aided diagnosis system for breast mammograms. Int J Adv Comput Sci Appl 8(7):286\u2013290","journal-title":"Int J Adv Comput Sci Appl"},{"key":"9716_CR76","doi-asserted-by":"publisher","first-page":"113","DOI":"10.17179\/excli201-701","volume":"16","author":"A Jalalian","year":"2017","unstructured":"Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan MIB, Ramli ARB (2017) Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. Exclus J 16:113\u2013137. \nhttps:\/\/doi.org\/10.17179\/excli201-701","journal-title":"Exclus J"},{"key":"9716_CR77","unstructured":"James JJ, Wilson ARM, Evans AJ (2016) The breast. Retrieved from \nhttps:\/\/radiologykey.com\/the-breast-2\/\n\n. Accessed 28 Aug 2018"},{"key":"9716_CR78","doi-asserted-by":"crossref","unstructured":"Jarrett K, Kavukcuoglu K, LeCun Y (2009) What is the best multi-stage architecture for object recognition? Paper presented at the 2009 IEEE 12th international conference on computer vision","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"9716_CR79","doi-asserted-by":"crossref","unstructured":"Jiang F, Liu H, Yu S, Xie Y (2017) Breast mass lesion classification in mammograms by transfer learning. Paper presented at the ACM international conference proceeding series","DOI":"10.1145\/3035012.3035022"},{"key":"9716_CR80","first-page":"3","volume":"2012","author":"H Jing","year":"2012","unstructured":"Jing H, Yang Y, Nishikawa RM (2012) Regularization in retrieval-driven classification of clustered microcalcifications for breast cancer. J Biomed Imaging 2012:3","journal-title":"J Biomed Imaging"},{"key":"9716_CR81","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/627892","volume":"2014","author":"S Jirayucharoensak","year":"2014","unstructured":"Jirayucharoensak S, Pan-Ngum S, Israsena P (2014) EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. Sci World J 2014:1\u201310","journal-title":"Sci World J"},{"key":"9716_CR82","doi-asserted-by":"crossref","unstructured":"Jyh-Horng C, Jinn Tsong T, Tung-Kuan L, Kao-Shing H, Hon-Yi S (2014) Predictive models for 5-year mortality after breast cancer surgery. Paper presented at the 2014 International conference on machine learning and cybernetics","DOI":"10.1109\/ICMLC.2014.7009084"},{"issue":"2","key":"9716_CR83","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s12193-015-0195-2","volume":"10","author":"SE Kahou","year":"2016","unstructured":"Kahou SE, Bouthillier X, Lamblin P, Gulcehre C, Michalski V, Konda K et al (2016) Emonets: multimodal deep learning approaches for emotion recognition in video. J Multimodal User Interfaces 10(2):99\u2013111","journal-title":"J Multimodal User Interfaces"},{"key":"9716_CR84","first-page":"37","volume":"4","author":"H Kasban","year":"2015","unstructured":"Kasban H, El-Bendary M, Salama D (2015) A comparative study of medical imaging techniques. Int J Inf Sci Intell Syst 4:37\u201358","journal-title":"Int J Inf Sci Intell Syst"},{"key":"9716_CR85","unstructured":"Keele S (2007) Guidelines\nfor performing systematic\nliterature reviews in software\nengineering. In: Technical\nreport, Ver. 2.3 EBSE\nTechnical Report. EBSE"},{"key":"9716_CR86","doi-asserted-by":"crossref","unstructured":"Khan MHM (2017) Automated breast cancer diagnosis using artificial neural network (ANN). Paper presented at the 2017 3rd Iranian conference on signal processing and intelligent systems, New York","DOI":"10.1109\/ICSPIS.2017.8311589"},{"issue":"6","key":"9716_CR87","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 (2014) A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng 61(6):1729\u20131738","journal-title":"IEEE Trans Biomed Eng"},{"key":"9716_CR88","doi-asserted-by":"crossref","unstructured":"Kim DH, Kim ST, Ro YM (2016) Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis. Paper presented at the 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP)","DOI":"10.1109\/ICASSP.2016.7471811"},{"key":"9716_CR89","unstructured":"Abdullah-Al N, Bin Ali F, Kong YN, IEEE (2017) Histopathological breast-image classification with image enhancement by convolutional neural network. Paper presented at the 2017 20th International conference of computer and information technology, New York"},{"key":"9716_CR90","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Paper presented at the Advances in neural information processing systems"},{"key":"9716_CR91","doi-asserted-by":"crossref","unstructured":"Kumar D, Kumar C, Shao M (2017a) Cross-database mammographic image analysis through unsupervised domain adaptation. Paper presented at the 2017 IEEE international conference on big data (big data)","DOI":"10.1109\/BigData.2017.8258419"},{"issue":"1","key":"9716_CR92","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.bbe.2017.01.001","volume":"37","author":"I Kumar","year":"2017","unstructured":"Kumar I, Bhadauria HS, Virmani J, Thakur S (2017b) A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern Biomed Eng 37(1):217\u2013228. \nhttps:\/\/doi.org\/10.1016\/j.bbe.2017.01.001","journal-title":"Biocybern Biomed Eng"},{"issue":"2","key":"9716_CR93","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1148\/radiol.2017162326","volume":"284","author":"P Lakhani","year":"2017","unstructured":"Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574\u2013582","journal-title":"Radiology"},{"issue":"5","key":"9716_CR94","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1109\/TPAMI.2007.70740","volume":"30","author":"TC Landgrebe","year":"2008","unstructured":"Landgrebe TC, Duin RP (2008) Efficient multiclass ROC approximation by decomposition via confusion matrix perturbation analysis. IEEE Trans Pattern Anal Mach Intell 30(5):810\u2013822","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"9716_CR95","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.cpet.2014.12.004","volume":"10","author":"L Lebron","year":"2015","unstructured":"Lebron L, Greenspan D, Pandit-Taskar N (2015) PET imaging of breast cancer: role in patient management. PET Clinics 10(2):159\u2013195. \nhttps:\/\/doi.org\/10.1016\/j.cpet.2014.12.004","journal-title":"PET Clinics"},{"issue":"12","key":"9716_CR96","doi-asserted-by":"publisher","first-page":"5356","DOI":"10.1016\/j.eswa.2015.02.005","volume":"42","author":"H Lee","year":"2015","unstructured":"Lee H, Chen Y-PP (2015) Image based computer aided diagnosis system for cancer detection. Expert Syst Appl 42(12):5356\u20135365. \nhttps:\/\/doi.org\/10.1016\/j.eswa.2015.02.005","journal-title":"Expert Syst Appl"},{"key":"9716_CR97","doi-asserted-by":"crossref","unstructured":"Leod PM, Verma B (2016) Polynomial prediction of neurons in neural network classifier for breast cancer diagnosis. Paper presented at the Proceedings\u2014international conference on natural computation","DOI":"10.1109\/ICNC.2015.7378089"},{"key":"9716_CR98","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388. \nhttps:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"issue":"4","key":"9716_CR99","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3390\/info8040131","volume":"8","author":"F Liu","year":"2017","unstructured":"Liu F, Hernandez-Cabronero M, Sanchez V, Marcellin MW, Bilgin A (2017) The current role of image compression standards in medical imaging. Information 8(4):131","journal-title":"Information"},{"issue":"1","key":"9716_CR100","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/0161734613507240","volume":"36","author":"C Lo","year":"2014","unstructured":"Lo C, Shen Y-W, Huang C-S, Chang R-F (2014) Computer-aided multiview tumor detection for automated whole breast ultrasound. Ultrason Imaging 36(1):3\u201317. \nhttps:\/\/doi.org\/10.1177\/0161734613507240","journal-title":"Ultrason Imaging"},{"key":"9716_CR101","doi-asserted-by":"crossref","unstructured":"Macenko M, Niethammer M, Marron JS, Borland D, Woosley JT, Guan X et al (2009) A method for normalizing histology slides for quantitative analysis. Paper presented at the IEEE international symposium on biomedical imaging: from nano to macro, 2009. ISBI\u201909","DOI":"10.1109\/ISBI.2009.5193250"},{"issue":"1","key":"9716_CR102","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1109\/MSP.2014.2346443","volume":"32","author":"MT McCann","year":"2015","unstructured":"McCann MT, Ozolek JA, Castro CA, Parvin B, Kovacevic J (2015) Automated histology analysis: opportunities for signal processing. IEEE Signal Process Mag 32(1):78\u201387","journal-title":"IEEE Signal Process Mag"},{"issue":"4","key":"9716_CR103","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115\u2013133. \nhttps:\/\/doi.org\/10.1007\/BF02478259","journal-title":"Bull Math Biophys"},{"key":"9716_CR104","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/2610628","author":"MM Mehdy","year":"2017","unstructured":"Mehdy MM, Ng PY, Shair EF, Saleh NIM, Gomes C (2017) Artificial neural networks in image processing for early detection of breast cancer. Comput Math Methods Med. \nhttps:\/\/doi.org\/10.1155\/2017\/2610628","journal-title":"Comput Math Methods Med"},{"key":"9716_CR105","doi-asserted-by":"crossref","unstructured":"Mendel KR, Li H, Sheth D, Giger ML (2018) Transfer learning with convolutional neural networks for lesion classification on clinical breast tomosynthesis. Paper presented at the Progress in biomedical optics and imaging\u2014proceedings of SPIE","DOI":"10.1117\/12.2294973"},{"key":"9716_CR106","unstructured":"MFMER (2018) Breast MRI. Retrieved from \nhttps:\/\/www.mayoclinic.org\/tests-procedures\/breast-mri\/about\/pac-20384809\n\n. Accessed 30 Aug 2018"},{"key":"9716_CR107","doi-asserted-by":"crossref","unstructured":"Mina LM, Mat Isa NA (2015) Breast abnormality detection in mammograms using artificial neural network. Paper presented at the I4CT 2015\u20142015 2nd international conference on computer, communications, and control technology, art proceeding","DOI":"10.1109\/I4CT.2015.7219577"},{"issue":"2","key":"9716_CR108","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.mric.2009.01.010","volume":"17","author":"M Moon","year":"2009","unstructured":"Moon M, Cornfeld D, Weinreb J (2009) Dynamic contrast-enhanced breast MR imaging. Magn Reson Imaging Clin N Am 17(2):351\u2013362","journal-title":"Magn Reson Imaging Clin N Am"},{"issue":"2","key":"9716_CR109","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","volume":"19","author":"IC Moreira","year":"2012","unstructured":"Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS (2012) Inbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236\u2013248","journal-title":"Acad Radiol"},{"issue":"4","key":"9716_CR110","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/s11548-013-0838-2","volume":"8","author":"DC Moura","year":"2013","unstructured":"Moura DC, L\u00f3pez MAG (2013) An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int J Comput Assist Radiol Surg 8(4):561\u2013574","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"9716_CR111","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7525-4","author":"G Murtaza","year":"2019","unstructured":"Murtaza G, Shuib L, Mujtaba G, Raza G (2019) Breast cancer multi-classification through deep neural network and hierarchical classification approach. Multimed Tools Appl. \nhttps:\/\/doi.org\/10.1007\/s11042-019-7525-4","journal-title":"Multimed Tools Appl"},{"key":"9716_CR112","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/3781951","author":"AA Nahid","year":"2017","unstructured":"Nahid AA, Kong Y (2017a) Involvement of machine learning for breast cancer image classification: a survey. Comput Math Methods Med. \nhttps:\/\/doi.org\/10.1155\/2017\/3781951","journal-title":"Comput Math Methods Med"},{"key":"9716_CR113","doi-asserted-by":"crossref","unstructured":"Nahid AA, Kong YA (2017b) Local and global feature utilization for breast image classification by convolutional neural network. Paper presented at the 2017 International conference on digital image computing\u2014techniques and applications, New York","DOI":"10.1109\/DICTA.2017.8227460"},{"key":"9716_CR114","doi-asserted-by":"publisher","DOI":"10.3390\/info9010019","author":"AA Nahid","year":"2018","unstructured":"Nahid AA, Kong Y (2018) Histopathological breast-image classification using local and frequency domains by convolutional neural network. Information (Switzerland). \nhttps:\/\/doi.org\/10.3390\/info9010019","journal-title":"Information (Switzerland)"},{"key":"9716_CR115","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/2362108","author":"AA Nahid","year":"2018","unstructured":"Nahid AA, Mehrabi MA, Kong Y (2018) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. Biomed Res Int. \nhttps:\/\/doi.org\/10.1155\/2018\/2362108","journal-title":"Biomed Res Int"},{"issue":"3","key":"9716_CR116","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1590\/2446-4740.04915","volume":"32","author":"CDL Nascimento","year":"2016","unstructured":"Nascimento CDL, Silva SDS, da Silva TA, Pereira WCA, Costa MGF, Costa Filho CFF (2016) Breast tumor classification in ultrasound images using support vector machines and neural networks. Revista Brasileira de Engenharia Biomedica 32(3):283\u2013292. \nhttps:\/\/doi.org\/10.1590\/2446-4740.04915","journal-title":"Revista Brasileira de Engenharia Biomedica"},{"key":"9716_CR117","doi-asserted-by":"crossref","unstructured":"Nejad EM, Affendey LS, Latip RB, Ishak IB (2017) Classification of histopathology images of breast into benign and malignant using a single-layer convolutional neural network. Paper presented at the ACM international conference proceeding series","DOI":"10.1145\/3132300.3132331"},{"key":"9716_CR118","doi-asserted-by":"crossref","unstructured":"Nweke HF, Teh YW, Alo UR, Mujtaba G (2018) Analysis of multi-sensor fusion for mobile and wearable sensor based human activity recognition. Paper presented at the Proceedings of the international conference on data processing and applications","DOI":"10.1145\/3224207.3224212"},{"key":"9716_CR119","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.inffus.2018.06.002","volume":"46","author":"HF Nweke","year":"2019","unstructured":"Nweke HF, Teh YW, Mujtaba G, Al-garadi MA (2019) Data fusion and multiple classifier systems for human activity detection and health monitoring: review and open research directions. Inf Fusion 46:147\u2013170","journal-title":"Inf Fusion"},{"key":"9716_CR120","doi-asserted-by":"crossref","unstructured":"Pack C, Shin S, Choi HD, Jeon SI, Kim J (2016) Optimized multilayer perceptron using dynamic learning rate based microwave tomography breast cancer screening. Paper presented at the proceedings of the ACM symposium on applied computing","DOI":"10.1145\/2851613.2851825"},{"key":"9716_CR121","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2016.08.103","volume":"229","author":"X Pan","year":"2017","unstructured":"Pan X, Li L, Yang H, Liu Z, Yang J, Zhao L, Fan Y (2017) Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks. Neurocomputing 229:88\u201399. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2016.08.103","journal-title":"Neurocomputing"},{"key":"9716_CR122","doi-asserted-by":"crossref","unstructured":"Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. Paper presented at the BMVC","DOI":"10.5244\/C.29.41"},{"key":"9716_CR123","doi-asserted-by":"crossref","unstructured":"Paula EL, Ladeira M, Carvalho RN, Marzag\u00e3o T (2016) Deep learning anomaly detection as support fraud investigation in brazilian exports and anti-money laundering. Paper presented at the 2016 15th IEEE international conference on machine learning and applications (ICMLA)","DOI":"10.1109\/ICMLA.2016.0172"},{"issue":"5","key":"9716_CR124","doi-asserted-by":"publisher","first-page":"751","DOI":"10.3233\/XST-16226","volume":"25","author":"Y Qiu","year":"2017","unstructured":"Qiu Y, Yan S, Gundreddy RR, Wang Y, Cheng S, Liu H, Zheng B (2017) A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J X-Ray Sci Technol 25(5):751\u2013763. \nhttps:\/\/doi.org\/10.3233\/XST-16226","journal-title":"J X-Ray Sci Technol"},{"key":"9716_CR125","unstructured":"Radiological Society of North America, I. R. (2018) RadiologyInfo for patients. Retrieved from \nhttps:\/\/www.radiologyinfo.org\/en\/info.cfm?pg=genus\n\n. Accessed 2 Sep 2018"},{"key":"9716_CR126","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.patcog.2017.08.004","volume":"72","author":"R Rasti","year":"2017","unstructured":"Rasti R, Teshnehlab M, Phung SL (2017) Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks. Pattern Recogn 72:381\u2013390. \nhttps:\/\/doi.org\/10.1016\/j.patcog.2017.08.004","journal-title":"Pattern Recogn"},{"key":"9716_CR127","unstructured":"Rebecca Sawyer Lee FG, Hoogi A, Rubin D (2016) Curated breast imaging subset of DDSM dataset. The Breast Cancer Imaging Archieve. Retrieved from \nhttps:\/\/wiki.cancerimagingarchive.net\/display\/Public\/CBIS-DDSM#4413fe70f2bb4159b326a3f07fa6e6a9\n\n. Accessed 10 Sep 2018"},{"issue":"5","key":"9716_CR128","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard E, Adhikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Graphics Appl 21(5):34\u201341","journal-title":"IEEE Comput Graphics Appl"},{"issue":"3","key":"9716_CR129","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1016\/j.eswa.2014.09.020","volume":"42","author":"R Rouhi","year":"2015","unstructured":"Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 42(3):990\u20131002. \nhttps:\/\/doi.org\/10.1016\/j.eswa.2014.09.020","journal-title":"Expert Syst Appl"},{"key":"9716_CR130","volume-title":"Rubin\u2019s pathology: clinicopathologic foundations of medicine","author":"R Rubin","year":"2008","unstructured":"Rubin R, Strayer DS, Rubin E (2008) Rubin\u2019s pathology: clinicopathologic foundations of medicine. Lippincott Williams & Wilkins, Philadelphia"},{"issue":"4","key":"9716_CR131","first-page":"291","volume":"23","author":"AC Ruifrok","year":"2001","unstructured":"Ruifrok AC, Johnston DA (2001) Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 23(4):291\u2013299","journal-title":"Anal Quant Cytol Histol"},{"issue":"3","key":"9716_CR132","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/j.ejrad.2009.08.024","volume":"77","author":"A Sadaf","year":"2011","unstructured":"Sadaf A, Crystal P, Scaranelo A, Helbich T (2011) Performance of computer-aided detection applied to full-field digital mammography in detection of breast cancers. Eur J Radiol 77(3):457\u2013461","journal-title":"Eur J Radiol"},{"key":"9716_CR133","unstructured":"Saidin N, Sakim HAM, Ngah UK, Shuaib IL (2012) Segmentation of breast regions in mammogram based on density: a review. arXiv preprint \narXiv:1209.5494"},{"issue":"23","key":"9716_CR134","doi-asserted-by":"publisher","first-page":"8894","DOI":"10.1088\/1361-6560\/aa93d4","volume":"62","author":"RK Samala","year":"2017","unstructured":"Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Cha KH, Richter CD (2017) Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys Med Biol 62(23):8894\u20138908. \nhttps:\/\/doi.org\/10.1088\/1361-6560\/aa93d4","journal-title":"Phys Med Biol"},{"issue":"9","key":"9716_CR135","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1088\/1361-6560\/aabb5b","volume":"63","author":"RK Samala","year":"2018","unstructured":"Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Richter C, Cha K (2018) Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. Phys Med Biol 63(9):8. \nhttps:\/\/doi.org\/10.1088\/1361-6560\/aabb5b","journal-title":"Phys Med Biol"},{"issue":"3","key":"9716_CR136","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1504\/IJMEI.2016.077446","volume":"8","author":"D Sathish","year":"2016","unstructured":"Sathish D, Kamath S, Rajagopal KV, Prasad K (2016) Medical imaging techniques and computer aided diagnostic approaches for the detection of breast cancer with an emphasis on thermography\u2014a review. Int J Med Eng Inf 8(3):275\u2013299. \nhttps:\/\/doi.org\/10.1504\/IJMEI.2016.077446","journal-title":"Int J Med Eng Inf"},{"key":"9716_CR137","unstructured":"Schneider M, Yaffe M (2000) Better detection: improving our chances. Paper presented at the Digital mammography: 5th international workshop on digital mammography IWDM"},{"key":"9716_CR138","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-319-61316-1_8","volume-title":"Biologically rationalized computing techniques for image processing applications","author":"D Selvathi","year":"2018","unstructured":"Selvathi D, Aarthy Poornila A (2018) Deep learning techniques for breast cancer detection using medical image analysis. In: Hemanth J, Balas VE (eds) Biologically rationalized computing techniques for image processing applications. Springer, Cham, pp 159\u2013186"},{"key":"9716_CR139","doi-asserted-by":"crossref","unstructured":"Sert E, Ertekin S, Halici U (2017) Ensemble of convolutional neural networks for classification of breast microcalcification from mammograms. Paper presented at the Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS","DOI":"10.1109\/EMBC.2017.8036918"},{"issue":"4","key":"9716_CR140","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1016\/j.ultrasmedbio.2015.11.016","volume":"42","author":"J Shan","year":"2016","unstructured":"Shan J, Alam SK, Garra B, Zhang YT, Ahmed T (2016) Computer-aided diagnosis for breast ultrasound using computerized Bi-rads features and machine learning methods. Ultrasound Med Biol 42(4):980\u2013988. \nhttps:\/\/doi.org\/10.1016\/j.ultrasmedbio.2015.11.016","journal-title":"Ultrasound Med Biol"},{"key":"9716_CR141","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221\u2013248. \nhttps:\/\/doi.org\/10.1146\/annurev-bioeng-071516-044442","journal-title":"Annu Rev Biomed Eng"},{"issue":"3","key":"9716_CR142","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/sym9030037","volume":"9","author":"MF Siddiqui","year":"2017","unstructured":"Siddiqui MF, Mujtaba G, Reza AW, Shuib L (2017) Multi-class disease classification in brain MRIs using a computer-aided diagnostic system. Symmetry 9(3):37","journal-title":"Symmetry"},{"key":"9716_CR144","doi-asserted-by":"crossref","unstructured":"Sivachitra M, Vijayachitra S (2015) Classification of post operative breast cancer patient information using complex valued neural classifiers. Paper presented at the 2015 International conference on cognitive computing and information processing (CCIP)","DOI":"10.1109\/CCIP.2015.7100717"},{"key":"9716_CR145","unstructured":"Sohn K, Zhou G, Lee C, Lee H (2013) Learning and selecting features jointly with point-wise gated Boltzmann machines. Paper presented at the Proceedings of the 30th international conference on international conference on machine learning\u2014volume 28, Atlanta, GA, USA"},{"key":"9716_CR146","unstructured":"Sophie Softley Pierce, P. M., Breast Cancer Care (2017) Three quarters of NHS Trusts and Health Boards say \u2018not enough\u2019 care for incurable breast cancer patients"},{"key":"9716_CR147","doi-asserted-by":"crossref","unstructured":"Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016a) Breast cancer histopathological image classification using convolutional neural networks. Paper presented at the Proceedings of the international joint conference on neural networks","DOI":"10.1109\/IJCNN.2016.7727519"},{"issue":"7","key":"9716_CR148","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 (2016b) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455\u20131462","journal-title":"IEEE Trans Biomed Eng"},{"key":"9716_CR149","doi-asserted-by":"crossref","unstructured":"Spanhol FA, Oliveira LS, Cavalin PR, Petitjean C, Heutte L (2017) Deep features for breast cancer histopathological image classification. Paper presented at the 2017 IEEE international conference on systems, man, and cybernetics (SMC)","DOI":"10.1109\/SMC.2017.8122889"},{"key":"9716_CR150","unstructured":"Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C et al (1994) The mammographic image analysis society digital mammogram database. Paper presented at the Exerpta Medica. International Congress series"},{"key":"9716_CR151","unstructured":"Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C et al (2015) Mammographic Image Analysis Society (MIAS) database v1, p 21"},{"key":"9716_CR152","doi-asserted-by":"crossref","unstructured":"Sun J, Binder A (2017) Comparison of deep learning architectures for H&E histopathology images. Paper presented at the 2017 IEEE conference on big data and analytics (ICBDA)","DOI":"10.1109\/ICBDAA.2017.8284105"},{"key":"9716_CR153","unstructured":"Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. Paper presented at the Advances in neural information processing systems"},{"key":"9716_CR154","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.compmedimag.2016.07.004","volume":"57","author":"W Sun","year":"2017","unstructured":"Sun W, Tseng TB, Zhang J, Qian W (2017) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graph\u00a057:4\u20139. \nhttps:\/\/doi.org\/10.1016\/j.compmedimag.2016.07.004","journal-title":"Comput Med Imaging Graph"},{"key":"9716_CR155","volume-title":"Introduction to reinforcement learning","author":"RS Sutton","year":"1998","unstructured":"Sutton RS, Barto AG (1998) Introduction to reinforcement learning, vol 135. MIT Press, Cambridge"},{"issue":"1","key":"9716_CR156","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/S0169-7439(97)00061-0","volume":"39","author":"D Svozil","year":"1997","unstructured":"Svozil D, Kvasnicka V, Pospichal J (1997) Introduction to multi-layer feed-forward neural networks. Chemometr Intell Lab Syst 39(1):43\u201362","journal-title":"Chemometr Intell Lab Syst"},{"issue":"11","key":"9716_CR157","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1016\/j.acra.2013.07.013","volume":"20","author":"T Tan","year":"2013","unstructured":"Tan T, Platel B, Twellmann T, van Schie G, Mus R, Grivegnee A et al (2013) Evaluation of the effect of computer-aided classification of benign and malignant lesions on reader performance in automated three-dimensional breast ultrasound. Acad Radiol 20(11):1381\u20131388. \nhttps:\/\/doi.org\/10.1016\/j.acra.2013.07.013","journal-title":"Acad Radiol"},{"key":"9716_CR158","doi-asserted-by":"crossref","unstructured":"Tataro\u011flu GA, Gen\u00e7 A, Kabak\u00e7\u0131 KA, \u00c7apar A, T\u00f6reyin BU, Ekenel HK et al (2017) A deep learning based approach for classification of CerbB2 tumor cells in breast cancer. Paper presented at the 2017 25th Signal processing and communications applications conference (SIU)","DOI":"10.1109\/SIU.2017.7960587"},{"key":"9716_CR159","unstructured":"Tessa S, Keith JFM (2018) The difference between an MRI and CT scan. Retrieved from \nhttps:\/\/www.healthline.com\/health\/ct-scan-vs-mri\n\n. Accessed 13 Sep  2018"},{"key":"9716_CR143","doi-asserted-by":"crossref","unstructured":"Ting FF, Sim KS, IEEE (2017) Self-regulated multilayer perceptron neural network for breast cancer classification. Paper presented at the 2017 International conference on robotics, automation and sciences, New York","DOI":"10.1109\/ICORAS.2017.8308074"},{"issue":"21","key":"9716_CR160","doi-asserted-by":"publisher","first-page":"6027","DOI":"10.1088\/0031-9155\/53\/21\/009","volume":"53","author":"P-H Tsui","year":"2008","unstructured":"Tsui P-H, Yeh C-K, Chang C-C, Liao Y-Y (2008) Classification of breast masses by ultrasonic Nakagami imaging: a feasibility study. Phys Med Biol 53(21):6027","journal-title":"Phys Med Biol"},{"key":"9716_CR161","doi-asserted-by":"publisher","first-page":"33075","DOI":"10.1038\/srep33075","volume":"6","author":"P-H Tsui","year":"2016","unstructured":"Tsui P-H, Ho M-C, Tai D-I, Lin Y-H, Wang C-Y, Ma H-Y (2016) Acoustic structure quantification by using ultrasound Nakagami imaging for assessing liver fibrosis. Sci Rep 6:33075","journal-title":"Sci Rep"},{"key":"9716_CR162","unstructured":"Ultrasound (2018) General ultrasound. Retrieved from \nhttps:\/\/www.radiologyinfo.org\/en\/info.cfm?pg=genus\n\n. Accessed 17 Sep 2018"},{"key":"9716_CR163","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-017-5280-3","author":"JCM van Zelst","year":"2018","unstructured":"van Zelst JCM, Tan T, Clauser P, Domingo A, Dorrius MD, Drieling D et al (2018) Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts. Eur Radiol. \nhttps:\/\/doi.org\/10.1007\/s00330-017-5280-3","journal-title":"Eur Radiol"},{"issue":"10","key":"9716_CR164","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1093\/annonc\/mds072","volume":"23","author":"JHMJ Vestjens","year":"2012","unstructured":"Vestjens JHMJ, Pepels MJ, de Boer M, Borm GF, van Deurzen CHM, van Diest PJ, Tjan-Heijnen VCG (2012) Relevant impact of central pathology review on nodal classification in individual breast cancer patients. Ann Oncol 23(10):2561\u20132566. \nhttps:\/\/doi.org\/10.1093\/annonc\/mds072","journal-title":"Ann Oncol"},{"key":"9716_CR165","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010a) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"9716_CR166","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010b) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"9716_CR167","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 (2017) Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 229:34\u201344. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2016.05.084","journal-title":"Neurocomputing"},{"key":"9716_CR168","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.dss.2017.11.001","volume":"105","author":"Y Wang","year":"2018","unstructured":"Wang Y, Xu W (2018) Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decis Support Syst 105:87\u201395","journal-title":"Decis Support Syst"},{"key":"9716_CR169","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.patcog.2018.01.009","volume":"78","author":"J Wang","year":"2018","unstructured":"Wang J, Yang Y (2018) A context-sensitive deep learning approach for microcalcification detection in mammograms. Pattern Recogn 78:12\u201322. \nhttps:\/\/doi.org\/10.1016\/j.patcog.2018.01.009","journal-title":"Pattern Recogn"},{"key":"9716_CR170","doi-asserted-by":"publisher","DOI":"10.1117\/1.jmi.1.3.034003","author":"H Wang","year":"2014","unstructured":"Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M et al (2014) Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging. \nhttps:\/\/doi.org\/10.1117\/1.jmi.1.3.034003","journal-title":"J Med Imaging"},{"key":"9716_CR171","doi-asserted-by":"crossref","unstructured":"Wang D, Wu K, Gu C, Guan X (2017) Time efficient cell detection in histopathology images using convolutional regression networks. Paper presented at the 2017 36th Chinese control conference (CCC)","DOI":"10.23919\/ChiCC.2017.8029106"},{"issue":"1","key":"9716_CR172","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"9716_CR173","unstructured":"World Health Organization (2018) Cancer. Retrieved from \nhttp:\/\/www.who.int\/en\/news-room\/fact-sheets\/detail\/cancer\n\n. Accessed 20 Sep 2018"},{"issue":"15","key":"9716_CR174","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1016\/j.ijleo.2014.01.114","volume":"125","author":"K Wu","year":"2014","unstructured":"Wu K, Chen X, Ding M (2014) Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound. Optik Int J Light Electron Opt 125(15):4057\u20134063","journal-title":"Optik Int J Light Electron Opt"},{"key":"9716_CR175","doi-asserted-by":"crossref","unstructured":"Wu J, Shi J, Li Y, Suo J, Zhang Q (2016) Histopathological image classification using random binary hashing based PCANet and bilinear classifier. Paper presented at the 2016 24th European signal processing conference (EUSIPCO)","DOI":"10.1109\/EUSIPCO.2016.7760609"},{"key":"9716_CR176","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neucom.2016.01.034","volume":"191","author":"J Xu","year":"2016","unstructured":"Xu J, Luo X, Wang G, Gilmore H, Madabhushi A (2016) A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191:214\u2013223. \nhttps:\/\/doi.org\/10.1016\/j.neucom.2016.01.034","journal-title":"Neurocomputing"},{"key":"9716_CR177","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-319-42999-1_6","volume-title":"Deep learning and convolutional neural networks for medical image computing: precision medicine, high performance and large-scale datasets","author":"J Xu","year":"2017","unstructured":"Xu J, Zhou C, Lang B, Liu Q (2017) Deep learning for histopathological image analysis: towards computerized diagnosis on cancers. In: Lu L, Zheng Y, Carneiro G, Yang L (eds) Deep learning and convolutional neural networks for medical image computing: precision medicine, high performance and large-scale datasets. Springer, Cham, pp 73\u201395"},{"key":"9716_CR178","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.cmpb.2017.12.012","volume":"156","author":"NIR Yassin","year":"2018","unstructured":"Yassin NIR, Omran S, El Houby EMF, Allam H (2018) Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput Methods Programs Biomed 156:25\u201345. \nhttps:\/\/doi.org\/10.1016\/j.cmpb.2017.12.012","journal-title":"Comput Methods Programs Biomed"},{"issue":"4","key":"9716_CR179","doi-asserted-by":"publisher","first-page":"300","DOI":"10.14366\/usg.17024","volume":"36","author":"JH Youk","year":"2017","unstructured":"Youk JH, Gweon HM, Son EJ (2017) Shear-wave elastography in breast ultrasonography: the state of the art. Ultrasonography 36(4):300\u2013309. \nhttps:\/\/doi.org\/10.14366\/usg.17024","journal-title":"Ultrasonography"},{"key":"9716_CR180","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.compbiomed.2018.04.004","volume":"96","author":"M Yousefi","year":"2018","unstructured":"Yousefi M, Krzy\u017cak A, Suen CY (2018) Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. Comput Biol Med 96:283\u2013293. \nhttps:\/\/doi.org\/10.1016\/j.compbiomed.2018.04.004","journal-title":"Comput Biol Med"},{"key":"9716_CR181","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.ultras.2016.08.004","volume":"72","author":"Q Zhang","year":"2016","unstructured":"Zhang Q, Xiao Y, Dai W, Suo JF, Wang CZ, Shi J, Zheng HR (2016) Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 72:150\u2013157. \nhttps:\/\/doi.org\/10.1016\/j.ultras.2016.08.004","journal-title":"Ultrasonics"},{"key":"9716_CR182","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, Liu J (2017) Whole mammogram image classification with convolutional neural networks. Paper presented at the 2017 IEEE international conference on bioinformatics and biomedicine (BIBM)","DOI":"10.1109\/BIBM.2017.8217738"},{"key":"9716_CR183","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.patcog.2017.05.010","volume":"71","author":"Y Zheng","year":"2017","unstructured":"Zheng Y, Jiang Z, Xie F, Zhang H, Ma Y, Shi H, Zhao Y (2017) Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification. Pattern Recogn 71:14\u201325. \nhttps:\/\/doi.org\/10.1016\/j.patcog.2017.05.010","journal-title":"Pattern Recogn"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-019-09716-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10462-019-09716-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-019-09716-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,23]],"date-time":"2020-05-23T23:06:15Z","timestamp":1590275175000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10462-019-09716-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,25]]},"references-count":183,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["9716"],"URL":"https:\/\/doi.org\/10.1007\/s10462-019-09716-5","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,25]]},"assertion":[{"value":"25 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors have no conflict of interests to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}