{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:09:18Z","timestamp":1771067358171,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597245","type":"print"},{"value":"9783030597252","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59725-2_20","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T15:02:49Z","timestamp":1601650969000},"page":"200-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["MommiNet: Mammographic Multi-view Mass Identification Networks"],"prefix":"10.1007","author":[{"given":"Zhicheng","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenjie","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanbo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shibin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"3","key":"20_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.6.3.031409","volume":"6","author":"R Agarwal","year":"2019","unstructured":"Agarwal, R., Diaz, O., Llad\u00f3, X., Yap, M.H., Mart\u00ed, R.: Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging 6(3), 1\u20139 (2019)","journal-title":"J. Med. Imaging"},{"issue":"6","key":"20_CR2","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1088\/0031-9155\/49\/6\/007","volume":"49","author":"R Campanini","year":"2004","unstructured":"Campanini, R., et al.: A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys. Med. Biol. 49(6), 961 (2004)","journal-title":"Phys. Med. Biol."},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Z., et al.: Deep learning based mass detection in mammograms. In: GlobalSIP, pp. 1\u20135 (2019)","DOI":"10.1109\/GlobalSIP45357.2019.8969485"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Cao, Z., et al.: DeepLIMa: deep learning based lesion identification in mammograms. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00047"},{"issue":"11","key":"20_CR5","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, A.P.: Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans. Med. Imaging 36(11), 2355\u20132365 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR6","unstructured":"Cunningham, D.: The Ups and Downs of Breasts, Physicians & Midwives (2013). https:\/\/physiciansandmidwives.com\/2013\/12\/11\/ups-and-downs-of-breasts\/"},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.cmpb.2018.01.007","volume":"156","author":"JOB Diniz","year":"2018","unstructured":"Diniz, J.O.B., Diniz, P.H.B., Valente, T.L.A., Silva, A.C., de Paiva, A.C., Gattass, M.: Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks. Comput. Methods Programs Biomed. 156, 191\u2013207 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"key":"20_CR8","unstructured":"Facebook: Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch (2019). https:\/\/github.com\/facebookresearch\/maskrcnn-benchmark"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Hu, H., Gu, J., Zhang, Z., Dai, J., Wei, Y.: Relation networks for object detection. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3588\u20133597 (2017)","DOI":"10.1109\/CVPR.2018.00378"},{"key":"20_CR10","unstructured":"Ikeda, D., Miyake, K.K.: Breast Imaging: The Requisites E-Book. Elsevier Health Sciences (2016)"},{"key":"20_CR11","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.inffus.2019.05.001","volume":"52","author":"A Jouirou","year":"2019","unstructured":"Jouirou, A., Ba\u00e2zaoui, A., Barhoumi, W.: Multi-view information fusion in mammograms: a comprehensive overview. Inf. Fusion 52, 308\u2013321 (2019)","journal-title":"Inf. Fusion"},{"key":"20_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"20_CR13","doi-asserted-by":"publisher","first-page":"170177","DOI":"10.1038\/sdata.2017.177","volume":"4","author":"RS Lee","year":"2017","unstructured":"Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4, 170177 (2017)","journal-title":"Sci. Data"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, D., Nailon, W.H., Davies, M.E., Laurenson, D.: A deep dual-path network for improved mammogram image processing. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1224\u20131228. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8682496"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, H., Zhang, L., Cheng, L.: Mammographic mass detection based on convolution neural network. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3850\u20133855. IEEE (2018)","DOI":"10.1109\/ICPR.2018.8545557"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"20_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/978-3-030-32226-7_53","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Y Liu","year":"2019","unstructured":"Liu, Y., et al.: From unilateral to bilateral learning: detecting mammogram masses with contrasted bilateral network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 477\u2013485. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_53"},{"key":"20_CR18","unstructured":"Ma, J., et al.: Cross-view relation networks for mammogram mass detection. arXiv abs\/1907.00528 (2019)"},{"key":"20_CR19","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","volume":"577","author":"S McKinney","year":"2020","unstructured":"McKinney, S., et al.: International evaluation of an AI system for breast cancer screening. Nature 577, 89\u201394 (2020)","journal-title":"Nature"},{"issue":"3","key":"20_CR20","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s10278-014-9739-3","volume":"28","author":"J de Nazar\u00e9 Silva","year":"2015","unstructured":"de Nazar\u00e9 Silva, J., de Carvalho Filho, A.O., Silva, A.C., De Paiva, A.C., Gattass, M.: Automatic detection of masses in mammograms using quality threshold clustering, correlogram function, and SVM. J. Digit. Imaging 28(3), 323\u2013337 (2015)","journal-title":"J. Digit. Imaging"},{"key":"20_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-3-030-00946-5_6","volume-title":"Image Analysis for Moving Organ, Breast, and Thoracic Images","author":"S Perek","year":"2018","unstructured":"Perek, S., Hazan, A., Barkan, E., Akselrod-Ballin, A.: Siamese network for dual-view mammography mass matching. In: Stoyanov, D., et al. (eds.) RAMBO\/BIA\/TIA -2018. LNCS, vol. 11040, pp. 55\u201363. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00946-5_6"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Ren, Y., et al.: Multiview mammographic mass detection based on a single shot detection system. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 109500E. International Society for Optics and Photonics (2019)","DOI":"10.1117\/12.2513136"},{"issue":"7Part1","key":"20_CR23","doi-asserted-by":"publisher","first-page":"2574","DOI":"10.1118\/1.2208919","volume":"33","author":"B Sahiner","year":"2006","unstructured":"Sahiner, B., et al.: Joint two-view information for computerized detection of microcalcifications on mammograms. Med. Phys. 33(7Part1), 2574\u20132585 (2006)","journal-title":"Med. Phys."},{"key":"20_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"20_CR25","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.patcog.2018.02.026","volume":"80","author":"H Wang","year":"2018","unstructured":"Wang, H., et al.: Breast mass classification via deeply integrating the contextual information from multi-view data. Pattern Recogn. 80, 42\u201352 (2018)","journal-title":"Pattern Recogn."},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"issue":"10","key":"20_CR27","doi-asserted-by":"publisher","first-page":"4451","DOI":"10.1118\/1.3220669","volume":"36","author":"J Wei","year":"2009","unstructured":"Wei, J., et al.: Computer-aided detection of breast masses on mammograms: dual system approach with two-view analysis. Med. Phys. 36(10), 4451\u20134460 (2009)","journal-title":"Med. Phys."},{"issue":"4","key":"20_CR28","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1109\/TMI.2019.2945514","volume":"39","author":"N Wu","year":"2019","unstructured":"Wu, N., et al.: Deep neural networks improve radiologists\u2019 performance in breast cancer screening. IEEE Trans. Med. Imaging 39(4), 1184\u20131194 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Xi, P., Shu, C., Goubran, R.: Abnormality detection in mammography using deep convolutional neural networks. In: 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/MeMeA.2018.8438639"},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, F., et al.: Cascaded generative and discriminative learning for microcalcification detection in breast mammograms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12578\u201312586 (2019)","DOI":"10.1109\/CVPR.2019.01286"},{"key":"20_CR31","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IOU loss: faster and better learning for bounding box regression. arXiv preprint arXiv:1911.08287 (2019)","DOI":"10.1609\/aaai.v34i07.6999"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59725-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:06:56Z","timestamp":1759356416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59725-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597245","9783030597252"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59725-2_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","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":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/en\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1809","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":"542","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":"30% - 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","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":"4","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":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}