{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T20:45:41Z","timestamp":1726173941487},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164361"},{"type":"electronic","value":"9783031164378"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16437-8_41","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"429-438","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Supervised Contrastive Learning to\u00a0Classify Paranasal Anomalies in\u00a0the\u00a0Maxillary Sinus"],"prefix":"10.1007","author":[{"given":"Debayan","family":"Bhattacharya","sequence":"first","affiliation":[]},{"given":"Benjamin Tobias","family":"Becker","sequence":"additional","affiliation":[]},{"given":"Finn","family":"Behrendt","sequence":"additional","affiliation":[]},{"given":"Marcel","family":"Bengs","sequence":"additional","affiliation":[]},{"given":"Dirk","family":"Beyersdorff","sequence":"additional","affiliation":[]},{"given":"Dennis","family":"Eggert","sequence":"additional","affiliation":[]},{"given":"Elina","family":"Petersen","sequence":"additional","affiliation":[]},{"given":"Florian","family":"Jansen","sequence":"additional","affiliation":[]},{"given":"Marvin","family":"Petersen","sequence":"additional","affiliation":[]},{"given":"Bastian","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Betz","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Schlaefer","sequence":"additional","affiliation":[]},{"given":"Anna Sophie","family":"Hoffmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"41_CR1","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. https:\/\/arxiv.org\/pdf\/2002.05709"},{"issue":"4","key":"41_CR2","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1017\/s0022215100115609","volume":"105","author":"LD Cooke","year":"1991","unstructured":"Cooke, L.D., Hadley, D.M.: MRI of the paranasal sinuses: incidental abnormalities and their relationship to symptoms. J. Laryngol. Otol. 105(4), 278\u2013281 (1991). https:\/\/doi.org\/10.1017\/s0022215100115609","journal-title":"J. Laryngol. Otol."},{"key":"41_CR3","unstructured":"Efron, B., Tibshirani, R.: An Introduction to the Bootstrap, Monographs on Statistics and Applied Probability, vol. 57, [nachdr.] edn. Chapman & Hall, Boca Raton (1998)"},{"key":"41_CR4","unstructured":"Falcon, F.N., et al.: Pytorch lightning, vol. 3. GitHub (2019). https:\/\/github.com\/PyTorchLightning\/pytorch-lightning"},{"key":"41_CR5","doi-asserted-by":"publisher","unstructured":"Hansen, A.G., et al.: Incidental findings in MRI of the paranasal sinuses in adults: a population-based study (hunt MRI). BMC Ear Nose Throat Disord. 14(1), 13 (2014). https:\/\/doi.org\/10.1186\/1472-6815-14-13","DOI":"10.1186\/1472-6815-14-13"},{"key":"41_CR6","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3D residual networks for action recognition. http:\/\/arxiv.org\/pdf\/1708.07632v1"},{"issue":"2","key":"41_CR7","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s10654-019-00577-4","volume":"35","author":"A Jagodzinski","year":"2019","unstructured":"Jagodzinski, A., et al.: Rationale and design of the Hamburg city health study. Eur. J. Epidemiol. 35(2), 169\u2013181 (2019). https:\/\/doi.org\/10.1007\/s10654-019-00577-4","journal-title":"Eur. J. Epidemiol."},{"key":"41_CR8","doi-asserted-by":"publisher","unstructured":"Jeon, Y., et al.: Deep learning for diagnosis of paranasal sinusitis using multi-view radiographs. Diagnost. (Basel Switz.) 11(2) (2021). https:\/\/doi.org\/10.3390\/diagnostics11020250","DOI":"10.3390\/diagnostics11020250"},{"key":"41_CR9","unstructured":"Khosla, P., et al.: Supervised contrastive learning. https:\/\/arxiv.org\/pdf\/2004.11362"},{"issue":"1","key":"41_CR10","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1097\/RLI.0000000000000503","volume":"54","author":"Y Kim","year":"2019","unstructured":"Kim, Y., et al.: Deep learning in diagnosis of maxillary sinusitis using conventional radiography. Invest. Radiol. 54(1), 7\u201315 (2019). https:\/\/doi.org\/10.1097\/RLI.0000000000000503","journal-title":"Invest. Radiol."},{"key":"41_CR11","doi-asserted-by":"publisher","unstructured":"Liu, G.S., et al.: Deep learning classification of inverted papilloma malignant transformation using 3d convolutional neural networks and magnetic resonance imaging. Int. Forum Allergy Rhinol. (2022). https:\/\/doi.org\/10.1002\/alr.22958","DOI":"10.1002\/alr.22958"},{"key":"41_CR12","doi-asserted-by":"publisher","unstructured":"Ma, Z., Yang, X.: Research on misdiagnosis of space occupying lesions in unilateral nasal sinus. Lin chuang er bi yan hou tou jing wai ke za zhi = J. Clin. Otorhinolaryngol. Head Neck Surg. 26(2), 59\u201361 (2012). https:\/\/doi.org\/10.13201\/j.issn.1001-1781.2012.02.005","DOI":"10.13201\/j.issn.1001-1781.2012.02.005"},{"key":"41_CR13","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(86), 2579\u20132605 (2008). http:\/\/jmlr.org\/papers\/v9\/vandermaaten08a.html"},{"key":"41_CR14","unstructured":"van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. CoRR abs\/1807.03748 (2018). http:\/\/arxiv.org\/abs\/1807.03748"},{"key":"41_CR15","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. https:\/\/arxiv.org\/pdf\/1912.01703"},{"issue":"2","key":"41_CR16","doi-asserted-by":"publisher","first-page":"381","DOI":"10.2214\/ajr.156.2.1898819","volume":"156","author":"KM Rak","year":"1991","unstructured":"Rak, K.M., Newell, J.D., Yakes, W.F., Damiano, M.A., Luethke, J.M.: Paranasal sinuses on MR images of the brain: significance of mucosal thickening. AJR Am. J. Roentgenol. 156(2), 381\u2013384 (1991). https:\/\/doi.org\/10.2214\/ajr.156.2.1898819","journal-title":"AJR Am. J. Roentgenol."},{"key":"41_CR17","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/1472-6831-12-30","volume":"12","author":"ICC Rege","year":"2012","unstructured":"Rege, I.C.C., Sousa, T.O., Leles, C.R., Mendon\u00e7a, E.F.: Occurrence of maxillary sinus abnormalities detected by cone beam CT in asymptomatic patients. BMC Oral Health 12, 30 (2012). https:\/\/doi.org\/10.1186\/1472-6831-12-30","journal-title":"BMC Oral Health"},{"key":"41_CR18","doi-asserted-by":"publisher","unstructured":"Stenner, M., Rudack, C.: Diseases of the nose and paranasal sinuses in child. GMS Curr. Top. Otorhinolaryngol. Head Neck Surg. 13, Doc10 (2014). https:\/\/doi.org\/10.3205\/cto000113","DOI":"10.3205\/cto000113"},{"issue":"1","key":"41_CR19","first-page":"33","volume":"38","author":"B Tarp","year":"2000","unstructured":"Tarp, B., Fiirgaard, B., Christensen, T., Jensen, J.J., Black, F.T.: The prevalence and significance of incidental paranasal sinus abnormalities on MRI. Rhinology 38(1), 33\u201338 (2000)","journal-title":"Rhinology"},{"key":"41_CR20","unstructured":"den van Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. https:\/\/arxiv.org\/pdf\/1807.03748"},{"issue":"9","key":"41_CR21","first-page":"641","volume":"110","author":"R Wilson","year":"2017","unstructured":"Wilson, R., Kuan Kok, H., Fortescue-Webb, D., Doody, O., Buckley, O., Torreggiani, W.C.: Prevalence and seasonal variation of incidental MRI paranasal inflammatory changes in an asymptomatic irish population. Ir. Med. J. 110(9), 641 (2017)","journal-title":"Ir. Med. J."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16437-8_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:08:24Z","timestamp":1710252504000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","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":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}