{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T15:16:41Z","timestamp":1761664601887,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031390586"},{"type":"electronic","value":"9783031390593"}],"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\/978-3-031-39059-3_15","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:01:37Z","timestamp":1690722097000},"page":"223-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Using Artificial Intelligence to\u00a0Reduce the\u00a0Risk of\u00a0Transfusion Hemolytic Reactions"],"prefix":"10.1007","author":[{"given":"Maya","family":"Trutschl","sequence":"first","affiliation":[]},{"given":"Urska","family":"Cvek","sequence":"additional","affiliation":[]},{"given":"Marjan","family":"Trutschl","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"15_CR1","unstructured":"Common objects in context, http:\/\/cocodataset.org\/"},{"key":"15_CR2","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI 2016, USENIX Association, USA, pp. 265\u2013283 (2016)"},{"issue":"9","key":"15_CR3","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1111\/j.1537-2995.2004.03427.x","volume":"44","author":"PA Arndt","year":"2004","unstructured":"Arndt, P.A., Garratty, G.: A retrospective analysis of the value of monocyte monolayer assay results for predicting the clinical significance of blood group alloantibodies. Transfusion 44(9), 1273\u20131281 (2004)","journal-title":"Transfusion"},{"key":"15_CR4","unstructured":"Ben-Kiki, O., Evans, C., d\u00f6t Net, I.: YAML ain\u2019t markup language (YAML\u2122) version 1.2.2 (2021). http:\/\/yaml.org\/spec\/1.2.2\/"},{"key":"15_CR5","unstructured":"Biewald, L.: Experiment tracking with weights and biases. Softw. Avail. wandb.com 2, 233 (2020)"},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-1-4842-4470-8_7","volume-title":"Building Machine Learning and Deep Learning Models on Google Cloud Platform","author":"E Bisong","year":"2019","unstructured":"Bisong, E.: Google colaboratory. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 59\u201364. Apress, Berkeley, CA (2019). https:\/\/doi.org\/10.1007\/978-1-4842-4470-8_7"},{"issue":"5","key":"15_CR7","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1093\/ajcp\/71.5.578","volume":"71","author":"LI Boral","year":"1979","unstructured":"Boral, L.I., Hill, S.S., Apollon, C.J., Folland, A.: The type and antibody screen, revisited. Am. J. Clin. Pathol. 71(5), 578\u2013581 (1979). https:\/\/doi.org\/10.1093\/ajcp\/71.5.578","journal-title":"Am. J. Clin. Pathol."},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Branch, D.R., Gallagher, M.T., Mison, A.P., Sy Siok Hian, A.L., Petz, L.D.: In vitro determination of red cell alloantibody significance using an assay of monocyte-macrophage interaction with sensitized erythrocytes. Br. J. Haematolo. 56(1), 19\u201329 (1984). https:\/\/doi.org\/10.1111\/j.1365-2141.1984.tb01268.x","DOI":"10.1111\/j.1365-2141.1984.tb01268.x"},{"issue":"2","key":"15_CR9","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020). https:\/\/doi.org\/10.3390\/info11020125","journal-title":"Information"},{"key":"15_CR10","unstructured":"Dwyer, B., Nelson, J., Solawetz, J.: Roboflow (2022). http:\/\/roboflow.com"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"7825","key":"15_CR12","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"C Harris","year":"2020","unstructured":"Harris, C., et al.: Array programming with NumPy. Nature 585(7825), 357\u2013362 (2020). https:\/\/doi.org\/10.1038\/s41586-020-2649-2","journal-title":"Nature"},{"key":"15_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Marquez, L.A.P., Chakrabarty, S.: Automatic image segmentation of monocytes and index computation using deep learning. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2656\u20132659 (2022). https:\/\/doi.org\/10.1109\/BIBM55620.2022.9994922","DOI":"10.1109\/BIBM55620.2022.9994922"},{"issue":"6","key":"15_CR15","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1046\/j.1537-2995.1987.27688071692.x","volume":"27","author":"S Nance","year":"1987","unstructured":"Nance, S., Arndt, P., Garratty, G.: Predicting the clinical significance of red cell alloantibodies using a monocyte monolayer assay. Transfusion 27(6), 449\u2013452 (1987). https:\/\/doi.org\/10.1046\/j.1537-2995.1987.27688071692.x","journal-title":"Transfusion"},{"key":"15_CR16","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)"},{"key":"15_CR17","doi-asserted-by":"publisher","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"}],"container-title":["Communications in Computer and Information Science","Deep Learning Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39059-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:04:41Z","timestamp":1690722281000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39059-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031390586","9783031390593"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39059-3_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeLTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Deep Learning Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"13 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"delta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/delta.scitevents.org\/","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":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","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":"9","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":"22","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":"21% - 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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}