{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:33:22Z","timestamp":1743028402721,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030850814"},{"type":"electronic","value":"9783030850821"}],"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\/978-3-030-85082-1_19","type":"book-chapter","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T16:16:47Z","timestamp":1628612207000},"page":"205-216","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Segmentation Quality Refinement in\u00a0Large-Scale Medical Image Dataset with\u00a0Crowd-Sourced Annotations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2733-4599","authenticated-orcid":false,"given":"Jan","family":"Cychnerski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7991-9022","authenticated-orcid":false,"given":"Tomasz","family":"Dziubich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,17]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","unstructured":"Barth, R., IJsselmuiden, J., Hemming, J., Van Henten, E.: Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation. Comput. Electron. Agric. 161, 291\u2013304 (2019). https:\/\/doi.org\/10.1016\/j.compag.2017.11.040. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0168169917307664. BigData and DSS in Agriculture","DOI":"10.1016\/j.compag.2017.11.040"},{"key":"19_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-28957-7_1","volume-title":"Computer Information Systems and Industrial Management","author":"A Brzeski","year":"2019","unstructured":"Brzeski, A., Grinholc, K., Nowodworski, K., Przyby\u0142ek, A.: Evaluating performance and accuracy improvements for attention-OCR. In: Saeed, K., Chaki, R., Janev, V. (eds.) CISIM 2019. LNCS, vol. 11703, pp. 3\u201311. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28957-7_1"},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Cabezas, F., Carlier, A., Charvillat, V., Salvador, A., Giro-I-Nieto, X.: Quality control in crowdsourced object segmentation. In: Proceedings - International Conference on Image Processing, ICIP 2015-December(May), pp. 4243\u20134247 (2015). https:\/\/doi.org\/10.1109\/ICIP.2015.7351606","DOI":"10.1109\/ICIP.2015.7351606"},{"key":"19_CR4","doi-asserted-by":"publisher","unstructured":"Cocos, A., Masino, A., Qian, T., Pavlick, E., Callison-Burch, C.: Effectively crowdsourcing radiology report annotations. In: Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis, pp. 109\u2013114. Association for Computational Linguistics, Lisbon, September 2015. https:\/\/doi.org\/10.18653\/v1\/W15-2614","DOI":"10.18653\/v1\/W15-2614"},{"key":"19_CR5","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD 1996, pp. 226\u2013231. AAAI Press (1996)"},{"key":"19_CR6","doi-asserted-by":"publisher","unstructured":"Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321\u2013331 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2018.09.013. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231218310749","DOI":"10.1016\/j.neucom.2018.09.013"},{"issue":"11","key":"19_CR7","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020). https:\/\/doi.org\/10.1145\/3422622","journal-title":"Commun. ACM"},{"key":"19_CR8","doi-asserted-by":"publisher","unstructured":"Heim, E., et al.: Large-scale medical image annotation with crowd-powered algorithms. J. Med. Imaging 5(03), 1 (2018). https:\/\/doi.org\/10.1117\/1.jmi.5.3.034002. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6129178\/","DOI":"10.1117\/1.jmi.5.3.034002"},{"issue":"4","key":"19_CR9","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/s10278-017-9976-3","volume":"30","author":"MD Kohli","year":"2017","unstructured":"Kohli, M.D., Summers, R.M., Geis, J.R.: Medical image data and datasets in the era of machine learning-whitepaper from the 2016 C-MIMI meeting dataset session. J. Digit. Imaging 30(4), 392\u2013399 (2017). https:\/\/doi.org\/10.1007\/s10278-017-9976-3","journal-title":"J. Digit. Imaging"},{"key":"19_CR10","doi-asserted-by":"publisher","unstructured":"Montagnon, E., et al.: Deep learning workflow in radiology: a primer (2020). https:\/\/doi.org\/10.1186\/s13244-019-0832-5","DOI":"10.1186\/s13244-019-0832-5"},{"key":"19_CR11","doi-asserted-by":"publisher","unstructured":"Press, W.H., Teukolsky, S.A., Vettering, W.T., Flannery, B.P.: Numerical Recipes the Art of Scientific Computing, 3rd edn. Cambridge University Press (2007). https:\/\/doi.org\/10.1017\/CBO9781107415324.004","DOI":"10.1017\/CBO9781107415324.004"},{"issue":"1","key":"19_CR12","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 29 (2015). https:\/\/doi.org\/10.1186\/s12880-015-0068-x","journal-title":"BMC Med. Imaging"},{"issue":"11","key":"19_CR13","doi-asserted-by":"publisher","first-page":"2459","DOI":"10.1109\/TMI.2016.2578680","volume":"35","author":"O Jimenez-del Toro","year":"2016","unstructured":"Jimenez-del Toro, O., et al.: Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: Visceral anatomy benchmarks. IEEE Trans. Med. Imaging 35(11), 2459\u20132475 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2578680","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR14","doi-asserted-by":"publisher","unstructured":"Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.: Understanding data augmentation for classification: when to warp? In: 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 (2016). https:\/\/doi.org\/10.1109\/DICTA.2016.7797091","DOI":"10.1109\/DICTA.2016.7797091"}],"container-title":["Communications in Computer and Information Science","New Trends in Database and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-85082-1_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:47:14Z","timestamp":1710348434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85082-1_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030850814","9783030850821"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85082-1_19","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"17 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADBIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Advances in Databases and Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tartu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Estonia","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":"24 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2021","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":"adbis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adbis2021.cs.ut.ee\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70","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":"18","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":"26% - 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)"}},{"value":"Workshops: submissions: 17,  papers accepted: DOING 2021: 3, SIMPDA 2021: 1, MADEISD 2021: 4, MegaData 2021: 1, CAoNS 2021: 2","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)"}}]}}