{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:56:11Z","timestamp":1743083771905,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319681948"},{"type":"electronic","value":"9783319681955"}],"license":[{"start":{"date-parts":[[2017,10,13]],"date-time":"2017-10-13T00:00:00Z","timestamp":1507852800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-68195-5_40","type":"book-chapter","created":{"date-parts":[[2017,10,12]],"date-time":"2017-10-12T01:14:26Z","timestamp":1507770866000},"page":"360-368","source":"Crossref","is-referenced-by-count":3,"title":["Lesion Classification in Mammograms Using Convolutional Neural Networks and Transfer Learning"],"prefix":"10.1007","author":[{"given":"Ana","family":"Perre","sequence":"first","affiliation":[]},{"given":"Lu\u00eds A.","family":"Alexandre","sequence":"additional","affiliation":[]},{"given":"Lu\u00eds C.","family":"Freire","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,10,13]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.cmpb.2015.12.014","volume":"127","author":"J Arevalo","year":"2016","unstructured":"Arevalo, J., Gonz\u00e1lez, F.A., Ramos-Poll\u00e1n, R., Oliveira, J.L., Guevara Lopez, M.A.: Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Progr. Biomed. 127, 248\u2013257 (2016)","journal-title":"Comput. Methods Progr. Biomed."},{"key":"40_CR2","unstructured":"Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Best Scientific Paper Award Return of the Devil in the Details: Delving Deep into Convolutional Nets British Machine Vision Conference (2014). arXiv.org. https:\/\/arxiv.org\/pdf\/1405.3531v4.pdf"},{"key":"40_CR3","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/RBME.2012.2232289","volume":"6","author":"K Ganesan","year":"2013","unstructured":"Ganesan, K., Acharya, U.R., Chua, C.K., Min, L.C., Abraham, K.T., Ng, K.H.: Computer-aided breast cancer detection using mammograms: a review. IEEE Rev. Biomed. Eng. 6, 77\u201397 (2013)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"40_CR4","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.clinimag.2012.09.024","volume":"37","author":"A Jalalian","year":"2013","unstructured":"Jalalian, A., Mashohor, S., Mahmud, H., Saripan, M., Ramli, A., Karasfi, B.: Computer-aided detection\/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imag. 37, 420\u2013426 (2013)","journal-title":"Clin. Imag."},{"key":"40_CR5","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding (2014). arXiv:1408.5093"},{"key":"40_CR6","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.neucom.2016.02.060","volume":"197","author":"Z Jiao","year":"2016","unstructured":"Jiao, Z., Gao, X., Wang, Y., Li, J.: A deep feature based framework for breast masses classification. Neurocomputing 197, 221\u2013231 (2016)","journal-title":"Neurocomputing"},{"key":"40_CR7","volume-title":"Imagiologia Bsica: Texto e Atlas","author":"JM Pisco","year":"2003","unstructured":"Pisco, J.M.: Imagiologia Bsica: Texto e Atlas, 1st edn. LIDEL, Lisboa (2003)","edition":"1"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Sampat, M., Markey, M., Bovik, A.: Computer-aided detection and diagnosis in mammography. In: Handbook of Image and Video Processing, 2nd edn., pp. 1195\u20131217. Academic, New York (2005)","DOI":"10.1016\/B978-012119792-6\/50130-3"},{"key":"40_CR9","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/JSYST.2014.2317378","volume":"8","author":"JS Tang","year":"2014","unstructured":"Tang, J.S., Agaian, S., Thompson, I.: Guest editorial: computer-aided detection or diagnosis (CAD) systems. IEEE Syst. J. 8, 907\u2013909 (2014)","journal-title":"IEEE Syst. J."},{"key":"40_CR10","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Lenc, K.: MatConvNet \u2013 convolutional neural networks for MATLAB. In: Proceeding of the ACM International Conferences on Multimedia (2015)","DOI":"10.1145\/2733373.2807412"},{"key":"40_CR11","unstructured":"Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. In: Cornell University Library (2016). arXiv.org . https:\/\/arxiv.org\/pdf\/1606.05718.pdf . Accessed 14 Mar 2017"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Wichakam, I., Vateekul, P.: Combining deep convolutional networks and SVMs for mass detection on digital mammograms. In: 2016 8th International Conference on Knowledge and Smart Technology (KST), pp. 239\u2013244 (2016)","DOI":"10.1109\/KST.2016.7440527"}],"container-title":["Lecture Notes in Computational Vision and Biomechanics","VipIMAGE 2017"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-68195-5_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T10:21:55Z","timestamp":1570184515000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-68195-5_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,13]]},"ISBN":["9783319681948","9783319681955"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-68195-5_40","relation":{},"ISSN":["2212-9391","2212-9413"],"issn-type":[{"type":"print","value":"2212-9391"},{"type":"electronic","value":"2212-9413"}],"subject":[],"published":{"date-parts":[[2017,10,13]]}}}