{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T05:38:15Z","timestamp":1767418695022,"version":"3.48.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049803"},{"type":"electronic","value":"9783032049810"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04981-0_28","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:13:32Z","timestamp":1758258812000},"page":"293-303","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Influence of\u00a0Classification Task and\u00a0Distribution Shift Type on\u00a0OOD Detection in\u00a0Fetal Ultrasound"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5528-9727","authenticated-orcid":false,"given":"Chun Kit","family":"Wong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3668-3128","authenticated-orcid":false,"given":"Anders N.","family":"Christensen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2628-2766","authenticated-orcid":false,"given":"Cosmin I.","family":"Bercea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6107-3009","authenticated-orcid":false,"given":"Julia A.","family":"Schnabel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9197-5564","authenticated-orcid":false,"given":"Martin G.","family":"Tolsgaard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9945-981X","authenticated-orcid":false,"given":"Aasa","family":"Feragen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"28_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-87735-4_12","volume-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis","author":"C Berger","year":"2021","unstructured":"Berger, C., Paschali, M., Glocker, B., Kamnitsas, K.: Confidence-based out-of-distribution detection: a comparative study and\u00a0analysis. In: Sudre, C.H., et al. (eds.) UNSURE\/PIPPI -2021. LNCS, vol. 12959, pp. 122\u2013132. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87735-4_12"},{"issue":"1","key":"28_CR2","doi-asserted-by":"publisher","first-page":"10200","DOI":"10.1038\/s41598-020-67076-5","volume":"10","author":"XP Burgos-Artizzu","year":"2020","unstructured":"Burgos-Artizzu, X.P., et al.: Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci. Rep. 10(1), 10200 (2020)","journal-title":"Sci. Rep."},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Combalia, M., Hueto, F., Puig, S., Malvehy, J., Vilaplana, V.: Uncertainty estimation in deep neural networks for dermoscopic image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 744\u2013745 (2020)","DOI":"10.1109\/CVPRW50498.2020.00380"},{"key":"28_CR4","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":"28_CR5","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321\u20131330. PMLR (2017)"},{"key":"28_CR6","unstructured":"Hong, Z., et\u00a0al.: Out-of-distribution detection in medical image analysis: a survey. arXiv preprint arXiv:2404.18279 (2024)"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Huang, L., Ruan, S., Xing, Y., Feng, M.: A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods. Med. Image Anal. 103223 (2024)","DOI":"10.1016\/j.media.2024.103223"},{"key":"28_CR8","first-page":"13956","volume":"36","author":"M Kirchhof","year":"2023","unstructured":"Kirchhof, M., Mucs\u00e1nyi, B., Oh, S.J., Kasneci, D.E.: Url: a representation learning benchmark for transferable uncertainty estimates. Adv. Neural. Inf. Process. Syst. 36, 13956\u201313980 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"28_CR9","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1038\/s41746-024-01085-w","volume":"7","author":"LM Koch","year":"2024","unstructured":"Koch, L.M., Baumgartner, C.F., Berens, P.: Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study. NPJ Digital Med. 7(1), 120 (2024)","journal-title":"NPJ Digital Med."},{"issue":"8","key":"28_CR10","doi-asserted-by":"publisher","DOI":"10.2196\/36427","volume":"10","author":"A Kurz","year":"2022","unstructured":"Kurz, A., et al.: Uncertainty estimation in medical image classification: systematic review. JMIR Med. Inform. 10(8), e36427 (2022)","journal-title":"JMIR Med. Inform."},{"key":"28_CR11","unstructured":"Lahlou, S., et al.: DEUP: direct epistemic uncertainty prediction. arXiv preprint arXiv:2102.08501 (2021)"},{"key":"28_CR12","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Lambert, B., Forbes, F., Doyle, S., Dehaene, H., Dojat, M.: Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis. Artif. Intell. Med. 102830 (2024)","DOI":"10.1016\/j.artmed.2024.102830"},{"key":"28_CR14","unstructured":"Lee, S., Purushwalkam, S., Cogswell, M., Crandall, D., Batra, D.: Why m heads are better than one: training a diverse ensemble of deep networks. arXiv preprint arXiv:1511.06314 (2015)"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Li, Z., Kamnitsas, K., Islam, M., Chen, C., Glocker, B.: Estimating model performance under domain shifts with class-specific confidence scores. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 693\u2013703. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-16449-1_66"},{"key":"28_CR16","unstructured":"Linmans, J., van\u00a0der Laak, J., Litjens, G.: Efficient out-of-distribution detection in digital pathology using multi-head convolutional neural networks. In: Medical Imaging with Deep Learning, pp. 465\u2013478. PMLR (2020)"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Mishra, D., Zhao, H., Saha, P., Papageorghiou, A.T., Noble, J.A.: Dual conditioned diffusion models for out-of-distribution detection: application to fetal ultrasound videos. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 216\u2013226. Springer, Cham (2023)","DOI":"10.1007\/978-3-031-43907-0_21"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Mucs\u00e1nyi, B., Kirchhof, M., Oh, S.J.: Benchmarking uncertainty disentanglement: Specialized uncertainties for specialized tasks. arXiv preprint arXiv:2402.19460 (2024)","DOI":"10.52202\/079017-1614"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Mukhoti, J., Kirsch, A., van Amersfoort, J., Torr, P.H., Gal, Y.: Deep deterministic uncertainty: a new simple baseline. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 24384\u201324394 (2023)","DOI":"10.1109\/CVPR52729.2023.02336"},{"issue":"1","key":"28_CR20","doi-asserted-by":"publisher","first-page":"2728","DOI":"10.1038\/s41598-023-29490-3","volume":"13","author":"C Sendra-Balcells","year":"2023","unstructured":"Sendra-Balcells, C., et al.: Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries. Sci. Rep. 13(1), 2728 (2023)","journal-title":"Sci. Rep."},{"issue":"1","key":"28_CR21","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Tardy, M., Scheffer, B., Mateus, D.: Uncertainty measurements for the reliable classification of mammograms. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 495\u2013503. Springer, Cham (2019)","DOI":"10.1007\/978-3-030-32226-7_55"},{"key":"28_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1007\/978-3-030-59710-8_80","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"J Thagaard","year":"2020","unstructured":"Thagaard, J., Hauberg, S., Vegt, B., Ebstrup, T., Hansen, J.D., Dahl, A.B.: Can you trust predictive uncertainty under real dataset shifts in digital pathology? In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 824\u2013833. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_80"},{"key":"28_CR24","unstructured":"Van\u00a0Amersfoort, J., Smith, L., Teh, Y.W., Gal, Y.: Uncertainty estimation using a single deep deterministic neural network. In: International Conference on Machine Learning, pp. 9690\u20139700. PMLR (2020)"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Yoo, D., Kweon, I.S.: Learning loss for active learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 93\u2013102 (2019)","DOI":"10.1109\/CVPR.2019.00018"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Zadorozhny, K., Thoral, P., Elbers, P., Cin\u00e0, G.: Out-of-distribution detection for medical applications: guidelines for practical evaluation. In: Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence, pp. 137\u2013153. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-14771-5_10"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Zou, K., Chen, Z., Yuan, X., Shen, X., Wang, M., Fu, H.: A review of uncertainty estimation and its application in medical imaging. Meta-Radiology, 100003 (2023)","DOI":"10.1016\/j.metrad.2023.100003"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04981-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T05:33:36Z","timestamp":1767418416000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04981-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049803","9783032049810"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04981-0_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}