{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:08:24Z","timestamp":1774314504809,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872335","type":"print"},{"value":"9783030872342","type":"electronic"}],"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\/978-3-030-87234-2_68","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"723-732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Location Constrained Dual-Branch Network for Reliable Diagnosis of Jaw Tumors and Cysts"],"prefix":"10.1007","author":[{"given":"Jiacong","family":"Hu","sequence":"first","affiliation":[]},{"given":"Zunlei","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Yining","family":"Mao","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Mingli","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"68_CR1","doi-asserted-by":"publisher","first-page":"99540","DOI":"10.1109\/ACCESS.2019.2929365","volume":"7","author":"F Altaf","year":"2019","unstructured":"Altaf, F., Islam, S., Akhtar, N., Janjua, N.K.: Going deep in medical image analysis: Concepts, methods, challenges, and future directions. IEEE Access 7, 99540\u201399572 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"68_CR2","doi-asserted-by":"publisher","first-page":"e118","DOI":"10.1016\/j.oooo.2015.05.002","volume":"120","author":"S Apajalahti","year":"2015","unstructured":"Apajalahti, S., Kelppe, J., Kontio, R., Hagstr\u00f6m, J.: Imaging characteristics of ameloblastomas and diagnostic value of computed tomography and magnetic resonance imaging in a series of 26 patients. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 120(2), e118\u2013e130 (2015)","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"issue":"4","key":"68_CR3","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.oooo.2019.05.014","volume":"128","author":"Y Ariji","year":"2019","unstructured":"Ariji, Y., et al.: Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 128(4), 424\u2013430 (2019)","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"key":"68_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L., Papandreou, G., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv (2017)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"68_CR5","unstructured":"Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020)"},{"issue":"1","key":"68_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5624\/isd.2011.41.1.1","volume":"41","author":"JW Choi","year":"2011","unstructured":"Choi, J.W.: Assessment of panoramic radiography as a national oral examination tool: review of the literature. Imaging Sci. Dent. 41(1), 1 (2011)","journal-title":"Imaging Sci. Dent."},{"key":"68_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"68_CR8","doi-asserted-by":"crossref","unstructured":"Feng, Z., et al.: Edge-competing pathological liver vessel segmentation with limited labels. In: AAAI Conference on Artificial Intelligence (2021)","DOI":"10.1609\/aaai.v35i2.16221"},{"issue":"2","key":"68_CR9","doi-asserted-by":"publisher","first-page":"205","DOI":"10.2334\/josnusd.50.205","volume":"50","author":"P Gonz\u00e1lez-Alva","year":"2008","unstructured":"Gonz\u00e1lez-Alva, P., et al.: Keratocystic odontogenic tumor: a retrospective study of 183 cases. J. Oral Sci. 50(2), 205\u2013212 (2008)","journal-title":"J. Oral Sci."},{"key":"68_CR10","doi-asserted-by":"publisher","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","volume":"6","author":"J Ker","year":"2018","unstructured":"Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375\u20139389 (2018)","journal-title":"IEEE Access"},{"issue":"8","key":"68_CR11","doi-asserted-by":"publisher","first-page":"20200185","DOI":"10.1259\/dmfr.20200185","volume":"49","author":"O Kwon","year":"2020","unstructured":"Kwon, O., et al.: Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofacial Radiol. 49(8), 20200185 (2020)","journal-title":"Dentomaxillofacial Radiol."},{"issue":"1","key":"68_CR12","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1111\/odi.13223","volume":"26","author":"JH Lee","year":"2020","unstructured":"Lee, J.H., Kim, D.H., Jeong, S.N.: Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral Dis. 26(1), 152\u2013158 (2020)","journal-title":"Oral Dis."},{"key":"68_CR13","unstructured":"Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation. In: BMVC, p. 285 (2018)"},{"issue":"2","key":"68_CR14","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1097\/00005537-199802000-00022","volume":"108","author":"JG Meara","year":"1998","unstructured":"Meara, J.G., Shah, S., Li, K.K., Cunningham, M.J.: The odontogenic keratocyst: a 20-year clinicopathologic review. Laryngoscope 108(2), 280\u2013283 (1998)","journal-title":"Laryngoscope"},{"issue":"3","key":"68_CR15","doi-asserted-by":"publisher","first-page":"236","DOI":"10.4258\/hir.2018.24.3.236","volume":"24","author":"W Poedjiastoeti","year":"2018","unstructured":"Poedjiastoeti, W., Suebnukarn, S.: Application of convolutional neural network in the diagnosis of jaw tumors. Healthcare Inform. Res. 24(3), 236 (2018)","journal-title":"Healthcare Inform. Res."},{"key":"68_CR16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"68_CR17","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"68_CR18","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:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"68_CR19","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 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"68_CR20","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"68_CR21","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"68_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"6","key":"68_CR23","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.3390\/jcm9061839","volume":"9","author":"H Yang","year":"2020","unstructured":"Yang, H., et al.: Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs. J. Clin. Med. 9(6), 1839 (2020)","journal-title":"J. Clin. Med."},{"key":"68_CR24","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR, pp. 6230\u20136239 (2017)","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87234-2_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:39:38Z","timestamp":1673311178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87234-2_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872335","9783030872342"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87234-2_68","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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":"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":"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":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.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":"1622","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":"531","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":"33% - 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.","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)"}}]}}