{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T20:02:27Z","timestamp":1726084947637},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030453848"},{"type":"electronic","value":"9783030453855"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-45385-5_67","type":"book-chapter","created":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T12:13:38Z","timestamp":1590581618000},"page":"750-760","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Breast Cancer Classification via Information and Multi-model Integration"],"prefix":"10.1007","author":[{"given":"J. C.","family":"Morales","sequence":"first","affiliation":[]},{"given":"Francisco","family":"Carrillo-Perez","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Castillo-Secilla","sequence":"additional","affiliation":[]},{"given":"Ignacio","family":"Rojas","sequence":"additional","affiliation":[]},{"given":"Luis Javier","family":"Herrera","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"issue":"1","key":"67_CR1","first-page":"7","volume":"69","author":"RL Siegel","year":"2019","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA: A Cancer J. Clin. 69(1), 7\u201334 (2019)","journal-title":"CA: A Cancer J. Clin."},{"key":"67_CR2","unstructured":"Breast Cancer. World Health Organization. World Health Organization (2018). \nhttps:\/\/www.who.int\/cancer\/prevention\/diagnosis-screening\/breast- cancer\/en\n\n. Accessed 17 Jan 2020"},{"key":"67_CR3","unstructured":"Number of new cases and deaths. National Cancer Institute (2018). \nhttps:\/\/seer.cancer.gov\/statfacts\/html\/breast.html\n\n. Accessed 17 Jan 2020"},{"issue":"2","key":"67_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0212127","volume":"14","author":"D Castillo","year":"2019","unstructured":"Castillo, D., et al.: Leukemia multiclass assessment and classification from microarray and RNA-seq technologies integration at gene expression level. PloS One 14(2), 1\u201325 (2019)","journal-title":"PloS One"},{"key":"67_CR5","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.compeleceng.2019.04.012","volume":"76","author":"\u015e \u00d6zt\u00fcrk","year":"2019","unstructured":"\u00d6zt\u00fcrk, \u015e., Akdemir, B.: HIC-net: a deep convolutional neural network model for classification of histopathological breast images. Comput. Electr. Eng. 76, 299\u2013310 (2019)","journal-title":"Comput. Electr. Eng."},{"key":"67_CR6","doi-asserted-by":"crossref","unstructured":"G\u00e1lvez, J.M., et al.: Towards improving skin cancer diagnosis by integrating microarray and RNA-seq datasets. IEEE J. Biomed. Health Inf. (2019)","DOI":"10.1109\/JBHI.2019.2953978"},{"key":"67_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2019.03.014","volume":"55","author":"T Qaiser","year":"2019","unstructured":"Qaiser, T., Tsang, Y.W., Taniyama, D., Sakamoto, N., Nakane, K., Epstein, D., Rajpoot, N.: Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features. Med. Image Anal. 55, 1\u201314 (2019)","journal-title":"Med. Image Anal."},{"key":"67_CR8","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.patcog.2018.07.022","volume":"84","author":"B Gecer","year":"2018","unstructured":"Gecer, B., Aksoy, S., Mercan, E., Shapiro, L.G., Weaver, D.L., Elmore, J.G.: Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recogn. 84, 345\u2013356 (2018)","journal-title":"Pattern Recogn."},{"key":"67_CR9","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2019.09.044","volume":"375","author":"Y Benhammou","year":"2020","unstructured":"Benhammou, Y., Achchab, B., Herrera, F., Tabik, S.: BreakHis based breast cancer automatic diagnosis using deep learning: taxonomy, survey and insights. Neurocomputing 375, 9\u201324 (2020)","journal-title":"Neurocomputing"},{"key":"67_CR10","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568\u2013576 (2014)"},{"key":"67_CR11","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo Vadis, action recognition? A new model and the kinetics dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299\u20136308 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"issue":"14","key":"67_CR12","doi-asserted-by":"publisher","first-page":"i446","DOI":"10.1093\/bioinformatics\/btz342","volume":"35","author":"A Cheerla","year":"2019","unstructured":"Cheerla, A., Gevaert, O.: Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35(14), i446\u2013i454 (2019)","journal-title":"Bioinformatics"},{"issue":"12","key":"67_CR13","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1056\/NEJMp1607591","volume":"375","author":"RL Grossman","year":"2016","unstructured":"Grossman, R.L., Heath, A.P., Ferretti, V., Varmus, H.E., Lowy, D.R., Kibbe, W.A., Staudt, L.M.: Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375(12), 1109\u20131112 (2016)","journal-title":"N. Engl. J. Med."},{"issue":"7553","key":"67_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"1","key":"67_CR15","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"67_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"67_CR17","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint \narXiv:1409.1556"},{"key":"67_CR18","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256, March 2010"},{"key":"67_CR19","unstructured":"Python Software Foundation. Python Language Reference, version 3.6. \nhttp:\/\/www.python.org"},{"key":"67_CR20","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024\u20138035 (2019)"},{"volume-title":"A Guide to NumPy","year":"2006","author":"TE Oliphant","key":"67_CR21","unstructured":"Oliphant, T.E.: A Guide to NumPy, vol. 1. Trelgol Publishing, USA (2006)"},{"volume-title":"Learning OpenCV: Computer Vision with the OpenCV Library","year":"2008","author":"G Bradski","key":"67_CR22","unstructured":"Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O\u2019Reilly Media Inc., Newton (2008)"},{"key":"67_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"67_CR24","unstructured":"Author: Aphex34, Date: 16 December 2015, Typical CNN Architecture. \nhttps:\/\/commons.wikimedia.org\/wiki\/File:Typical_cnn.png"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics and Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45385-5_67","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,27]],"date-time":"2020-05-27T12:19:43Z","timestamp":1590581983000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-45385-5_67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030453848","9783030453855"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45385-5_67","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWBBIO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Bioinformatics and Biomedical Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 May 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 May 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwbbio2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwbbio.ugr.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}