{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T20:46:58Z","timestamp":1769546818365,"version":"3.49.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031179785","type":"print"},{"value":"9783031179792","type":"electronic"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17979-2_11","type":"book-chapter","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T23:04:00Z","timestamp":1664492640000},"page":"108-117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust Colorectal Polyp Characterization Using a\u00a0Hybrid Bayesian Neural Network"],"prefix":"10.1007","author":[{"given":"Nikoo","family":"Dehghani","sequence":"first","affiliation":[]},{"given":"Thom","family":"Scheeve","sequence":"additional","affiliation":[]},{"given":"Quirine E. W.","family":"van der Zander","sequence":"additional","affiliation":[]},{"given":"Ayla","family":"Thijssen","sequence":"additional","affiliation":[]},{"given":"Ramon-Michel","family":"Schreuder","sequence":"additional","affiliation":[]},{"given":"Ad A. M.","family":"Masclee","sequence":"additional","affiliation":[]},{"given":"Erik J.","family":"Schoon","sequence":"additional","affiliation":[]},{"given":"Fons","family":"van der Sommen","sequence":"additional","affiliation":[]},{"given":"Peter H. N.","family":"de With","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,30]]},"reference":[{"key":"11_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. Clinic. 71(3), 209\u2013249 (2021)","DOI":"10.3322\/caac.21660"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"40950","DOI":"10.1109\/ACCESS.2018.2856402","volume":"6","author":"Y Shin","year":"2018","unstructured":"Shin, Y., Qadir, H.A., Aabakken, L., Bergsland, J., Balasingham, I.: Automatic colon polyp detection using region based deep CNN and post learning approaches. IEEE Access 6, 40950\u201340962 (2018)","journal-title":"IEEE Access"},{"issue":"1","key":"11_CR3","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1515\/biol-2020-0055","volume":"15","author":"J Meng","year":"2020","unstructured":"Meng, J., et al.: Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN. Open Life Sci. 15(1), 588\u2013596 (2020)","journal-title":"Open Life Sci."},{"issue":"1","key":"11_CR4","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/JBHI.2016.2635662","volume":"21","author":"R Zhang","year":"2016","unstructured":"Zhang, R., et al.: Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE J. Biomed. Health Inf. 21(1), 41\u201347 (2016)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"11_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101619","volume":"60","author":"K Wickstr\u00f8m","year":"2020","unstructured":"Wickstr\u00f8m, K., Kampffmeyer, M., Jenssen, R.: Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. Med. Image Anal. 60, 101619 (2020)","journal-title":"Med. Image Anal."},{"key":"11_CR6","unstructured":"Alam, S., Tomar, N.K., Thakur, A., Jha, D., Rauniyar, A.: Automatic polyp segmentation using u-net-resnet50. arXiv preprint arXiv:2012.15247 (2020)"},{"issue":"02","key":"11_CR7","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1055\/a-1372-0419","volume":"54","author":"J Weigt","year":"2022","unstructured":"Weigt, J., et al.: Performance of a new integrated computer-assisted system (CADe\/CADx) for detection and characterization of colorectal neoplasia. Endoscopy. 54(02), 180\u2013184 (2022)","journal-title":"Endoscopy."},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1016\/j.procs.2020.09.325","volume":"176","author":"H Usami","year":"2020","unstructured":"Usami, H., et al.: Colorectal polyp classification based on latent sharing features domain from multiple endoscopy images. Proc. Comput. Sci. 176, 2507\u20132514 (2020)","journal-title":"Proc. Comput. Sci."},{"issue":"15","key":"11_CR9","doi-asserted-by":"publisher","first-page":"5040","DOI":"10.3390\/app10155040","volume":"10","author":"R Fonoll\u00e0","year":"2020","unstructured":"Fonoll\u00e0, R., et al.: A CNN CADx system for multimodal classification of colorectal polyps combining WL, BLI, and LCI modalities. Appl. Sci. 10(15), 5040 (2020)","journal-title":"Appl. Sci."},{"key":"11_CR10","unstructured":"Guo, C., Pleiss, G., Sun, Y. and Weinberger, K.Q.: On calibration of modern neural networks. In: PLMR, International Conference on Machine Learning, pp. 1321\u20131330 (2017)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Kusters, K.C., et al.: Colorectal polyp classification using confidence-calibrated convolutional neural networks. In: SPIE, Medical Imaging 2022: Computer-Aided Diagnosis, vol. 12033, pp. 442\u2013454(2022)","DOI":"10.1117\/12.2606801"},{"key":"11_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101653","volume":"62","author":"G Carneiro","year":"2020","unstructured":"Carneiro, G., Pu, L.Z.C.T., Singh, R., Burt, A.: Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy. Med. Image Anal. 62, 101653 (2020)","journal-title":"Med. Image Anal."},{"key":"11_CR13","unstructured":"Krishnan, R., Subedar, M. and Tickoo, O.: BAR: Bayesian activity recognition using variational inference. arXiv preprint arXiv:1811.03305 (2018)"},{"key":"11_CR14","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"11_CR15","first-page":"1","volume":"30","author":"A Kendall","year":"2017","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30, 1\u201311 (2017)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"11_CR16","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: International Conference on Machine Learning, PMLR (2015)"},{"key":"11_CR17","unstructured":"Tan, M. and Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"11_CR18","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: Computer Vision and Pattern Recognition Conference, CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"11_CR19","unstructured":"Nazarovs, J., Mehta, R.R., Lokhande, V.S., Singh, V.: Graph reparameterizations for enabling 1000+ Monte Carlo iterations in Bayesian deep neural networks. In: Uncertainty in Artificial Intelligence, pp. 118\u2013128. PMLR (2021)"},{"key":"11_CR20","unstructured":"Wen, Y., Vicol, P., Ba, J., Tran, D., Grosse, R.: Flipout: efficient pseudo-independent weight perturbations on mini-batches. arXiv preprint arXiv:1803.04386 (2018)"},{"issue":"1\u20132","key":"11_CR21","first-page":"12","volume":"32","author":"MH DeGroot","year":"1983","unstructured":"DeGroot, M.H., Fienberg, S.E.: The comparison and evaluation of forecasters. J. R. Statist. Soc. Ser. D (The Statist.) 32(1\u20132), 12\u201322 (1983)","journal-title":"J. R. Statist. Soc. Ser. D (The Statist.)"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Niculescu-Mizil, A., Caruana, R.: Predicting good probabilities with supervised learning. In: 22nd International Conference on Machine Learning (2005)","DOI":"10.1145\/1102351.1102430"}],"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\/978-3-031-17979-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:03:52Z","timestamp":1709831032000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17979-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031179785","9783031179792"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17979-2_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"30 September 2022","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":"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":"22 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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caption2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/caption-workshop.github.io","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":"21","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":"16","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":"76% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}