{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:39:54Z","timestamp":1742967594662,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031453496"},{"type":"electronic","value":"9783031453502"}],"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:\/\/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:\/\/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\/978-3-031-45350-2_9","type":"book-chapter","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T06:02:35Z","timestamp":1696572155000},"page":"107-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Colonoscopy Coverage Revisited: Identifying Scanning Gaps in\u00a0Real-Time"],"prefix":"10.1007","author":[{"given":"George","family":"Leifman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Idan","family":"Kligvasser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman","family":"Goldenberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ehud","family":"Rivlin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Elad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,7]]},"reference":[{"issue":"8","key":"9_CR1","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.3390\/electronics9081188","volume":"9","author":"I Adjabi","year":"2020","unstructured":"Adjabi, I., Ouahabi, A., Benzaoui, A., Taleb-Ahmed, A.: Past, present, and future of face recognition: a review. Electronics 9(8), 1188 (2020)","journal-title":"Electronics"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Ali, S., Rittscher, J.: Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 91\u201395. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759450"},{"key":"9_CR3","first-page":"1","volume":"32","author":"P Bachman","year":"2019","unstructured":"Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. Adv. Neural Inf. Process. Syst. 32, 1\u201311 (2019)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Bae, G., et al.: Digiface-1m: 1 million digital face images for face recognition. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3526\u20133535 (2023)","DOI":"10.1109\/WACV56688.2023.00352"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Berton, G., Masone, C., Paolicelli, V., Caputo, B.: Viewpoint invariant dense matching for visual geolocalization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12169\u201312178 (2021)","DOI":"10.1109\/ICCV48922.2021.01195"},{"key":"9_CR6","unstructured":"Brownlee, J.: XGBoost With python: gradient boosted trees with XGBoost and scikit-learn. In: Machine Learning Mastery (2016)"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Lagadec, B., Dantcheva, A., Bremond, F.: Joint generative and contrastive learning for unsupervised person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2004\u20132013 (2021)","DOI":"10.1109\/CVPR46437.2021.00204"},{"key":"9_CR8","unstructured":"Chen, R.J., Bobrow, T.L., Athey, T., Mahmood, F., Durr, N.J.: Slam endoscopy enhanced by adversarial depth prediction. arXiv preprint arXiv:1907.00283 (2019)"},{"key":"9_CR9","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"issue":"11","key":"9_CR11","doi-asserted-by":"publisher","first-page":"3451","DOI":"10.1109\/TMI.2020.2994221","volume":"39","author":"D Freedman","year":"2020","unstructured":"Freedman, D., et al.: Detecting deficient coverage in colonoscopies. IEEE Trans. Med. Imaging 39(11), 3451\u20133462 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735\u20131742. IEEE (2006)","DOI":"10.1109\/CVPR.2006.100"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"9_CR15","unstructured":"Kelner, O., Weinstein, O., Rivlin, E., Goldenberg, R.: Motion-based weak supervision for video parsing with application to colonoscopy. In: Proceedings of the \u201cWhat is Motion for?\u201d Workshop, ECCV (2022)"},{"issue":"3","key":"9_CR16","doi-asserted-by":"publisher","first-page":"411","DOI":"10.5217\/ir.2017.15.3.411","volume":"15","author":"NH Kim","year":"2017","unstructured":"Kim, N.H., et al.: Miss rate of colorectal neoplastic polyps and risk factors for missed polyps in consecutive colonoscopies. Intest. Res. 15(3), 411 (2017)","journal-title":"Intest. Res."},{"key":"9_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"2","key":"9_CR18","doi-asserted-by":"publisher","first-page":"342","DOI":"10.3390\/s20020342","volume":"20","author":"Y Kortli","year":"2020","unstructured":"Kortli, Y., Jridi, M., Al Falou, A., Atri, M.: Face recognition systems: a survey. Sensors 20(2), 342 (2020)","journal-title":"Sensors"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Belongie, S., Hays, J.: Cross-view image geolocalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 891\u2013898 (2013)","DOI":"10.1109\/CVPR.2013.120"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Cui, Y., Belongie, S., Hays, J.: Learning deep representations for ground-to-aerial geolocalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5007\u20135015 (2015)","DOI":"10.1109\/CVPR.2015.7299135"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Lin, Y., Xie, L., Wu, Y., Yan, C., Tian, Q.: Unsupervised person re-identification via softened similarity learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3390\u20133399 (2020)","DOI":"10.1109\/CVPR42600.2020.00345"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Ma, R., et al.: Colon10k: a benchmark for place recognition in colonoscopy. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1279\u20131283. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9433780"},{"key":"9_CR23","doi-asserted-by":"publisher","first-page":"119533","DOI":"10.1109\/ACCESS.2021.3108234","volume":"9","author":"M Oliveira","year":"2021","unstructured":"Oliveira, M., Araujo, H., Figueiredo, I.N., Pinto, L., Curto, E., Perdigoto, L.: Registration of consecutive frames from wireless capsule endoscopy for 3d motion estimation. IEEE Access 9, 119533\u2013119545 (2021)","journal-title":"IEEE Access"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Posner, E., Zholkover, A., Frank, N., Bouhnik, M.: C3 fusion: consistent contrastive colon fusion, towards deep slam in colonoscopy. arXiv preprint arXiv:2206.01961 (2022)","DOI":"10.1007\/978-3-031-46914-5_2"},{"issue":"7","key":"9_CR25","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/TPAMI.2018.2846566","volume":"41","author":"F Radenovi\u0107","year":"2018","unstructured":"Radenovi\u0107, F., Tolias, G., Chum, O.: Fine-tuning CNN image retrieval with no human annotation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1655\u20131668 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"7","key":"9_CR26","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1007\/s11548-019-01962-w","volume":"14","author":"A Rau","year":"2019","unstructured":"Rau, A., et al.: Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy. Int. J. Comput. Assist. Radiol. Surg. 14(7), 1167\u20131176 (2019)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"9_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102338","volume":"77","author":"S Shao","year":"2022","unstructured":"Shao, S., et al.: Self-supervised monocular depth and ego-motion estimation in endoscopy: appearance flow to the rescue. Med. Image Anal. 77, 102338 (2022)","journal-title":"Med. Image Anal."},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Pwc-net: cnns for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8934\u20138943 (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, S.: Unsupervised person re-identification via multi-label classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10981\u201310990 (2020)","DOI":"10.1109\/CVPR42600.2020.01099"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2495\u20132504 (2021)","DOI":"10.1109\/CVPR46437.2021.00252"},{"key":"9_CR31","unstructured":"Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: Workshop on Faces in \u2018Real-Life\u2019 Images: Detection, Alignment, and Recognition (2008)"},{"issue":"4","key":"9_CR32","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1109\/TPAMI.2020.2975798","volume":"43","author":"C Yan","year":"2020","unstructured":"Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1445\u20131451 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"9_CR33","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TMRB.2020.3044108","volume":"3","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Zhao, L., Huang, S., Ye, M., Hao, Q.: A template-based 3d reconstruction of colon structures and textures from stereo colonoscopic images. IEEE Trans. Med. Rob. Bionics 3(1), 85\u201395 (2020)","journal-title":"IEEE Trans. Med. Rob. Bionics"}],"container-title":["Lecture Notes in Computer Science","Cancer Prevention Through Early Detection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45350-2_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:04:09Z","timestamp":1730264649000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45350-2_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453496","9783031453502"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45350-2_9","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":"7 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CaPTion","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Cancer Prevention through Early Detection","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caption2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/caption-workshop.github.io\/miccai2023\/","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":"CTM","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","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":"11","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":"92% - 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":"3.16","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":"2.23","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)"}}]}}