{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:49:01Z","timestamp":1742928541046,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031278174"},{"type":"electronic","value":"9783031278181"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/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":[[2023]]},"DOI":"10.1007\/978-3-031-27818-1_33","type":"book-chapter","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T10:20:17Z","timestamp":1680171617000},"page":"399-410","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-view Adaptive Bone Activation from\u00a0Chest X-Ray with\u00a0Conditional Adversarial Nets"],"prefix":"10.1007","author":[{"given":"Chaoqun","family":"Niu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jizhe","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tu","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huili","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weibo","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiancheng","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"33_CR1","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.compmedimag.2016.04.002","volume":"51","author":"S CandemirS","year":"2016","unstructured":"CandemirS, S., et al.: Atlas-based rib-bone detection in chest x-rays. Comput. Med. Imaging Graph. 51, 32\u201339 (2016)","journal-title":"Comput. Med. Imaging Graph."},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Chen, S., Suzuki, K.: Computerized detection of lung nodules by means of \u201cvirtual dual-energy\u201d radiography. IEEE Transactions on Biomedical Engineering 60(2), 369\u2013378 (2012)","DOI":"10.1109\/TBME.2012.2226583"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution. In: ICCV, pp. 3435\u20133444 (2019)","DOI":"10.1109\/ICCV.2019.00353"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: CVPR, vol. 1, pp. 539\u2013546 (2005)","DOI":"10.1109\/CVPR.2005.202"},{"issue":"7","key":"33_CR5","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1109\/TMI.2020.2974159","volume":"39","author":"M Eslami","year":"2020","unstructured":"Eslami, M., Tabarestani, S., Albarqouni, S., Adeli, E., Navab, N., Adjouadi, M.: Image-to-images translation for multi-task organ segmentation and bone suppression in chest x-ray radiography. IEEE Trans. Med. Imaging 39(7), 2553\u20132565 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"33_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102369","volume":"77","author":"L Han","year":"2022","unstructured":"Han, L., Lyu, Y., Peng, C., Zhou, S.K.: Gan-based disentanglement learning for chest x-ray rib suppression. Med. Image Anal. 77, 102369 (2022)","journal-title":"Med. Image Anal."},{"key":"33_CR7","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"},{"key":"33_CR8","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local Nash equilibrium. In: NeurIPS, pp. 6626\u20136637 (2017)"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: SIMBAD, pp. 84\u201392 (2015)","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"issue":"3","key":"33_CR12","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1148\/radiol.11110192","volume":"261","author":"F Li","year":"2011","unstructured":"Li, F., Engelmann, R., Pesce, L.L., Doi, K., Metz, C.E., MacMahon, H.: Small lung cancers: improved detection by use of bone suppression imaging-comparison with dual-energy subtraction chest radiography. Radiology 261(3), 937 (2011)","journal-title":"Radiology"},{"key":"33_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105014","volume":"180","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Zhang, X., Cai, G., Chen, Y., Yun, Z., Feng, Q., Yang, W.: Automatic delineation of ribs and clavicles in chest radiographs using fully convolutional densenets. Comput. Methods Programs Biomed. 180, 105014 (2019)","journal-title":"Comput. Methods Programs Biomed."},{"key":"33_CR14","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)"},{"key":"33_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"33_CR16","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"33_CR17","first-page":"1","volume":"66","author":"C Tang","year":"2021","unstructured":"Tang, C., He, Z., Li, Y., Lv, J.: Zero-shot learning via structure-aligned generative adversarial network. IEEE Trans. Neural Netw. Learn. Syst. 66, 1\u201314 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Van Ginneken, B., Ter Haar Romeny, B.M.: Automatic delineation of ribs in frontal chest radiographs. In: Medical Imaging 2000: Image Processing, vol. 3979, pp. 825\u2013836 (2000)","DOI":"10.1117\/12.387746"},{"issue":"23","key":"33_CR19","doi-asserted-by":"publisher","first-page":"18173","DOI":"10.1007\/s00500-020-05073-6","volume":"24","author":"J Wang","year":"2020","unstructured":"Wang, J., Lv, J., Yang, X., Tang, C., Peng, X.: Multimodal image-to-image translation between domains with high internal variability. Soft. Comput. 24(23), 18173\u201318184 (2020)","journal-title":"Soft. Comput."},{"key":"33_CR20","unstructured":"Wechsler, H.: Automatic Detection Of Rib Contours in Chest Radiographs. University of California, Irvine (1975)"},{"key":"33_CR21","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.media.2016.08.004","volume":"35","author":"W Yang","year":"2017","unstructured":"Yang, W., et al.: Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Med. Image Anal. 35, 421\u2013433 (2017)","journal-title":"Med. Image Anal."},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-27818-1_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T10:06:16Z","timestamp":1729159576000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/978-3-031-27818-1_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031278174","9783031278181"],"references-count":22,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-031-27818-1_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bergen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 January 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 January 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2023","order":10,"name":"conference_id","label":"Conference ID","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":"Conftool Pro","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"267","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":"86","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":"32% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}