{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:17:11Z","timestamp":1742912231173,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030875886"},{"type":"electronic","value":"9783030875893"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87589-3_19","type":"book-chapter","created":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T07:02:35Z","timestamp":1632553355000},"page":"180-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Active Learning for Dual-View Mammogram Analysis"],"prefix":"10.1007","author":[{"given":"Yutong","family":"Yan","sequence":"first","affiliation":[]},{"given":"Pierre-Henri","family":"Conze","sequence":"additional","affiliation":[]},{"given":"Mathieu","family":"Lamard","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Gwenol\u00e9","family":"Quellec","sequence":"additional","affiliation":[]},{"given":"B\u00e9atrice","family":"Cochener","sequence":"additional","affiliation":[]},{"given":"Gouenou","family":"Coatrieux","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. (2021)","DOI":"10.3322\/caac.21660"},{"key":"19_CR2","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., et al.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303\u2013312 (2017)","journal-title":"Med. Image Anal."},{"key":"19_CR3","doi-asserted-by":"publisher","first-page":"112855","DOI":"10.1016\/j.eswa.2019.112855","volume":"139","author":"VK Singh","year":"2020","unstructured":"Singh, V.K., et al.: Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst. Appl. 139, 112855 (2020)","journal-title":"Expert Syst. Appl."},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Yan, Y., Conze, P.H., Quellec, G., Lamard, M., Cochener, B., Coatrieux, G.: Two-stage multi-scale mass segmentation from full mammograms. In: IEEE International Symposium on Biomedical Imaging (2021)","DOI":"10.1109\/ISBI48211.2021.9433946"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Yan, Y., et al.: Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in high-resolution mammograms. In: IEEE International Engineering in Medicine and Biology (2019)","DOI":"10.1109\/EMBC.2019.8857167"},{"key":"19_CR6","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, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal. 37, 114\u2013128 (2017)","journal-title":"Med. Image Anal."},{"issue":"3","key":"19_CR7","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":"1","key":"19_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-22437-z","volume":"8","author":"D Ribli","year":"2018","unstructured":"Ribli, D., Horv\u00e1th, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 1\u20137 (2018)","journal-title":"Sci. Rep."},{"issue":"2","key":"19_CR9","first-page":"131","volume":"7","author":"SM Vijayarajan","year":"2014","unstructured":"Vijayarajan, S.M., Jaganathan, P.: Breast cancer segmentation and detection using multi-view mammogram. Acad. J. Cancer Res. 7(2), 131\u2013140 (2014)","journal-title":"Acad. J. Cancer Res."},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Yan, Y., Conze, P.H., Lamard, M., Quellec, G., Cochener, B., Coatrieux, G.: Multi-tasking siamese networks for breast mass detection using dual-view mammogram matching. In: International Workshop on Machine Learning in Medical Imaging, pp. 312\u2013321 (2020)","DOI":"10.1007\/978-3-030-59861-7_32"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"102083","DOI":"10.1016\/j.media.2021.102083","volume":"71","author":"Y Yan","year":"2021","unstructured":"Yan, Y., Conze, P.-H., Lamard, M., Quellec, G., Cochener, B., Coatrieux, G.: Towards improved breast mass detection using dual-view mammogram matching. Med. Image Anal. 71, 102083 (2021)","journal-title":"Med. Image Anal."},{"key":"19_CR12","unstructured":"Perek, S., Hazan, A., Barkan, E., Akselrod-Ballin, A.: Mammography dual view mass correspondence. arXiv preprint arXiv:1807.00637 (2018)"},{"key":"19_CR13","unstructured":"Ma, J., et al.: Cross-view relation networks for mammogram mass detection. arXiv preprint arXiv:1907.00528 (2019)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Gu, X., Shi, Z., Ma, J.: Multi-view learning for mammogram analysis: auto-diagnosis models for breast cancer. In: IEEE International Conference on Smart Internet of Things, pp. 149\u2013153 (2018)","DOI":"10.1109\/SmartIoT.2018.00035"},{"key":"19_CR15","unstructured":"Budd, S., Robinson, E.C., Kainz, B.: A survey on active learning and human-in-the-loop deep learning for medical image analysis. arXiv preprint arXiv:1910.02923 (2019)"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Shen, H., et al..: Deep active learning for breast cancer segmentation on immunohistochemistry images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 509\u2013518 (2020)","DOI":"10.1007\/978-3-030-59722-1_49"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Li, H., Yin, Z.: Attention, suggestion and annotation: a deep active learning framework for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 3\u201313 (2020)","DOI":"10.1007\/978-3-030-59710-8_1"},{"issue":"4","key":"19_CR18","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1007\/s40846-018-0437-3","volume":"39","author":"Yu Zhao","year":"2019","unstructured":"Zhao, Yu., Chen, D., Xie, H., Zhang, S., Lixu, G.: Mammographic image classification system via active learning. J. Med. Biol. Eng. 39(4), 569\u2013582 (2019)","journal-title":"J. Med. Biol. Eng."},{"key":"19_CR19","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.future.2019.07.013","volume":"101","author":"R Shen","year":"2019","unstructured":"Shen, R., Yan, K., Tian, K., Jiang, C., Zhou, K.: Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning. Future Gener. Comput. Syst. 101, 668\u2013679 (2019)","journal-title":"Future Gener. Comput. Syst."},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J. :Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Conze, P.H., et al.: Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks. Artif. Intell. Med. (2021)","DOI":"10.1016\/j.artmed.2021.102109"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., Lin, D.: Libra R-CNN: towards balanced learning for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 821\u2013830 (2019)","DOI":"10.1109\/CVPR.2019.00091"},{"key":"19_CR25","doi-asserted-by":"publisher","first-page":"170177","DOI":"10.1038\/sdata.2017.177","volume":"4","author":"R Lee","year":"2017","unstructured":"Lee, R., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4, 170177 (2017)","journal-title":"Sci. Data"},{"key":"19_CR26","unstructured":"Moreira, I.C., et al.: INbreast: toward a full-field digital mammographic database. Acad. Radiol. (2012)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87589-3_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,10]],"date-time":"2022-04-10T15:08:53Z","timestamp":1649603333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87589-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030875886","9783030875893"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87589-3_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2021\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"92","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":"71","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":"77% - 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":"2","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The workshop was held virtually.","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)"}}]}}