{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T20:37:05Z","timestamp":1770237425677,"version":"3.49.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030009458","type":"print"},{"value":"9783030009465","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-00946-5_7","type":"book-chapter","created":{"date-parts":[[2018,9,11]],"date-time":"2018-09-11T09:00:03Z","timestamp":1536656403000},"page":"64-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Large-Scale Mammography CAD with Deformable Conv-Nets"],"prefix":"10.1007","author":[{"given":"Stephen","family":"Morrell","sequence":"first","affiliation":[]},{"given":"Zbigniew","family":"Wojna","sequence":"additional","affiliation":[]},{"given":"Can Son","family":"Khoo","sequence":"additional","affiliation":[]},{"given":"Sebastien","family":"Ourselin","sequence":"additional","affiliation":[]},{"given":"Juan Eugenio","family":"Iglesias","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,12]]},"reference":[{"key":"7_CR1","unstructured":"American Cancer Society: What are the key statistics about breast cancer?"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.1001\/jamainternmed.2015.5231","volume":"175","author":"C Lehman","year":"2015","unstructured":"Lehman, C., Wellman, R., Buist, D., Kerlikowske, K., Tosteson, A., Miglioretti, D.: Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern. Med. 175, 1828 (2015)","journal-title":"JAMA Intern. Med."},{"issue":"3","key":"7_CR3","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1001\/jamainternmed.2013.1397","volume":"173","author":"CP Gross","year":"2013","unstructured":"Gross, C.P., et al.: The cost of breast cancer screening in the medicare population. JAMA Intern. Med. 173(3), 220 (2013)","journal-title":"JAMA Intern. Med."},{"key":"7_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-319-46723-8_5","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"M Jiang","year":"2016","unstructured":"Jiang, M., Zhang, S., Zheng, Y., Metaxas, D.N.: Mammographic mass segmentation with online learned shape and appearance priors. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 35\u201343. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_5"},{"issue":"5","key":"7_CR5","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/42.538938","volume":"15","author":"N Karssemeijer","year":"1996","unstructured":"Karssemeijer, N., te Brake, G.M.: Detection of stellate distortions in mammograms. IEEE Trans. Med. Imaging 15(5), 611\u2013619 (1996)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-319-46723-8_13","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"N Dhungel","year":"2016","unstructured":"Dhungel, N., Carneiro, G., Bradley, A.P.: The automated learning of deep features for breast mass classification from mammograms. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 106\u2013114. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_13"},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","volume":"35","author":"T Kooi","year":"2017","unstructured":"Kooi, T., Litjens, G., Ginneken, B.V., Gubern-m\u00e9rida, A., S\u00e1nchez, C.I., Mann, R., Heeten, A.D., Karssemeijer, N.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303\u2013312 (2017)","journal-title":"Med. Image Anal."},{"issue":"3","key":"7_CR8","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211\u2013252 (2015)","journal-title":"IJCV"},{"key":"7_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"7_CR10","unstructured":"Royal Surrey County Hospital: The Optimam Mammography Image Database"},{"key":"7_CR11","unstructured":"Sage Bionetworks: The Digital Mammography DREAM Challenge (2016)"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: CVPR, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, vol. 1, p. 4 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988. IEEE (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"7_CR15","unstructured":"Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object Detection via Region-based Fully Convolutional Networks, May 2016"},{"key":"7_CR16","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761\u2013769 (2015)","DOI":"10.1109\/CVPR.2016.89"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Shlens, J., Wojna, Z.: Rethinking the Inception Architecture for Computer Vision. (2015)","DOI":"10.1109\/CVPR.2016.308"},{"key":"7_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1007\/978-3-319-24574-4_78","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"G Carneiro","year":"2015","unstructured":"Carneiro, G., Nascimento, J., Bradley, A.P.: Unregistered multiview mammogram analysis with pre-trained deep learning models. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 652\u2013660. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_78"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-Excitation Networks (2017)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks, 1\u201312. arXiv preprint (2016)","DOI":"10.1109\/CVPR.2017.243"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated Residual Transformations for Deep Neural Networks (2016)","DOI":"10.1109\/CVPR.2017.634"},{"key":"7_CR23","unstructured":"Canziani, A., Paszke, A., Culurciello, E.: An analysis of deep neural network models for practical applications, 7p. Arxiv (2016)"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Lecture Notes in Computer Science","Image Analysis for Moving Organ, Breast, and Thoracic Images"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00946-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:05:00Z","timestamp":1694390700000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00946-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030009458","9783030009465"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00946-5_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Breast Image Analysis","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bia2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cs.adelaide.edu.au\/~bia2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.1","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"n\/a","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}