{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T22:16:37Z","timestamp":1777932997460,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":9,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819713349","type":"print"},{"value":"9789819713356","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-1335-6_5","type":"book-chapter","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T19:02:17Z","timestamp":1709665337000},"page":"52-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-task Learning Approach for\u00a0Unified Biometric Estimation from\u00a0Fetal Ultrasound Anomaly Scans"],"prefix":"10.1007","author":[{"given":"Mohammad Areeb","family":"Qazi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed Talha","family":"Alam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibrahim","family":"Almakky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Werner Gerhard","family":"Diehl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leanne","family":"Bricker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Yaqub","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"issue":"2","key":"5_CR1","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/S2352-4642(21)00311-4","volume":"6","author":"J Perin","year":"2022","unstructured":"Perin, J., et al.: Global, regional, and national causes of under-5 mortality in 2000\u201319: an updated systematic analysis with implications for the sustainable development goals. Lancet Child Adolescent Health 6(2), 106\u2013115 (2022)","journal-title":"Lancet Child Adolescent Health"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1007\/s00330-018-5695-5","volume":"29","author":"L Joskowicz","year":"2019","unstructured":"Joskowicz, L., Cohen, D., Caplan, N., Sosna, J.: Inter-observer variability of manual contour delineation of structures in CT. Eur. Radiol. 29, 1391\u20131399 (2019)","journal-title":"Eur. Radiol."},{"key":"5_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100723","volume":"26","author":"A Anaya-Isaza","year":"2021","unstructured":"Anaya-Isaza, A., Mera-Jim\u00e9nez, L., Zequera-Diaz, M.: An overview of deep learning in medical imaging. Inf. Med. Unlocked 26, 100723 (2021)","journal-title":"Inf. Med. Unlocked"},{"key":"5_CR4","doi-asserted-by":"publisher","first-page":"1098205","DOI":"10.3389\/fmed.2023.1098205","volume":"10","author":"MM Seval","year":"2023","unstructured":"Seval, M.M., Varl\u0131, B.: Current developments in artificial intelligence from obstetrics and gynecology to urogynecology. Front. Med. 10, 1098205 (2023)","journal-title":"Front. Med."},{"key":"5_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102629","volume":"83","author":"MC Fiorentino","year":"2023","unstructured":"Fiorentino, M.C., Villani, F.P., Cosmo, M.D., Frontoni, E., Moccia, S.: A review on deep-learning algorithms for fetal ultrasound-image analysis. Med. Image Anal. 83, 102629 (2023)","journal-title":"Med. Image Anal."},{"key":"5_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"5_CR7","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR9","unstructured":"Paszke, A., et\u00a0al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-1335-6_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T19:05:48Z","timestamp":1709665548000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-1335-6_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819713349","9789819713356"],"references-count":9,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-1335-6_5","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"value":"1876-1100","type":"print"},{"value":"1876-1119","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"9 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}