{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:24:25Z","timestamp":1742941465470,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"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_11","type":"book-chapter","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T06:02:35Z","timestamp":1696572155000},"page":"132-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Barrett\u2019s Esophagus Progression Using Geometric Variational Autoencoders"],"prefix":"10.1007","author":[{"given":"Vivien","family":"van Veldhuizen","sequence":"first","affiliation":[]},{"given":"Sharvaree","family":"Vadgama","sequence":"additional","affiliation":[]},{"given":"Onno","family":"de Boer","sequence":"additional","affiliation":[]},{"given":"Sybren","family":"Meijer","sequence":"additional","affiliation":[]},{"given":"Erik J.","family":"Bekkers","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,7]]},"reference":[{"key":"11_CR1","unstructured":"Arvanitidis, G., Hansen, L.K., Hauberg, S.: Latent space oddity: on the curvature of deep generative models. arXiv preprint arXiv:1710.11379 (2017)"},{"key":"11_CR2","unstructured":"Bachmann, G., B\u00e9cigneul, G., Ganea, O.: Constant curvature graph convolutional networks. In: International Conference on Machine Learning, pp. 486\u2013496. PMLR (2020)"},{"key":"11_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1007\/978-3-030-00928-1_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"EJ Bekkers","year":"2018","unstructured":"Bekkers, E.J., Lafarge, M.W., Veta, M., Eppenhof, K.A.J., Pluim, J.P.W., Duits, R.: Roto-translation covariant convolutional networks for medical image analysis. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 440\u2013448. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_50"},{"key":"11_CR4","unstructured":"Chadebec, C., Mantoux, C., Allassonni\u00e8re, S.: Geometry-aware hamiltonian variational auto-encoder (2020)"},{"key":"11_CR5","unstructured":"Chen, N., Klushyn, A., Kurle, R., Jiang, X., Bayer, J., Smagt, P.: Metrics for deep generative models. In: International Conference on Artificial Intelligence and Statistics, pp. 1540\u20131550. PMLR (2018)"},{"key":"11_CR6","unstructured":"Cohen, T., Welling, M.: Group equivariant convolutional networks. In: International Conference on Machine Learning, pp. 2990\u20132999. PMLR (2016)"},{"key":"11_CR7","unstructured":"Davidson, T.R., Falorsi, L., De Cao, N., Kipf, T., Tomczak, J.M.: Hyperspherical variational auto-encoders. arXiv preprint arXiv:1804.00891 (2018)"},{"key":"11_CR8","unstructured":"Gu, A., Sala, F., Gunel, B., R\u00e9, C.: Learning mixed-curvature representations in product spaces. In: International Conference on Learning Representations (2018)"},{"issue":"6","key":"11_CR9","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1002\/ueg2.12233","volume":"10","author":"M Hussein","year":"2022","unstructured":"Hussein, M., et al.: A new artificial intelligence system successfully detects and localises early neoplasia in barrett\u2019s esophagus by using convolutional neural networks. United Eur. Gastroenterol. J. 10(6), 528\u2013537 (2022)","journal-title":"United Eur. Gastroenterol. J."},{"key":"11_CR10","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"11_CR11","unstructured":"Lafarge, M.W., Pluim, J.P., Veta, M.: Orientation-disentangled unsupervised representation learning for computational pathology. arXiv preprint arXiv:2008.11673 (2020)"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Shao, H., Kumar, A., Thomas Fletcher, P.: The riemannian geometry of deep generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 315\u2013323 (2018)","DOI":"10.1109\/CVPRW.2018.00071"},{"key":"11_CR13","unstructured":"Skopek, O., Ganea, O.E., B\u00e9cigneul, G.: Mixed-curvature variational autoencoders. arXiv preprint arXiv:1911.08411 (2019)"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.compbiomed.2018.03.014","volume":"96","author":"LA de Souza Jr","year":"2018","unstructured":"de Souza Jr, L.A., et al.: A survey on barrett\u2019s esophagus analysis using machine learning. Comput. Biol. Med. 96, 203\u2013213 (2018)","journal-title":"Comput. Biol. Med."},{"key":"11_CR15","unstructured":"Tosi, A., Hauberg, S., Vellido, A., Lawrence, N.D.: Metrics for probabilistic geometries. arXiv preprint arXiv:1411.7432 (2014)"},{"key":"11_CR16","unstructured":"Vadgama, S., Tomczak, J.M., Bekkers, E.J.: Kendall shape-vae: learning shapes in a generative framework. In: NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations (2022)"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Van der Wel, M., Jansen, M., Vieth, M., Meijer, S.: What makes an expert barret\u2019s histopathologist?, vol. 908, pp. 137\u2013159 (2016)","DOI":"10.1007\/978-3-319-41388-4_8"},{"issue":"5","key":"11_CR18","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1136\/gutjnl-2019-318985","volume":"69","author":"MJ van der Wel","year":"2020","unstructured":"van der Wel, M.J., Coleman, H.G., Bergman, J.J., Jansen, M., Meijer, S.L.: Histopathologist features predictive of diagnostic concordance at expert level among a large international sample of pathologists diagnosing barret\u2019s dysplasia using digital pathology. Gut 69(5), 811\u2013822 (2020)","journal-title":"Gut"}],"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_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T06:05:40Z","timestamp":1696572340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45350-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453496","9783031453502"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45350-2_11","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)"}}]}}