{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T10:59:09Z","timestamp":1774263549348,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Hainan Province Clinical Medical Center","award":["(Project No. OWYH202175)"],"award-info":[{"award-number":["(Project No. OWYH202175)"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["(project No. YG2025QNA07)"],"award-info":[{"award-number":["(project No. YG2025QNA07)"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sanya Science and Technology Innovation Program","award":["(Project No. 2022KJCX41"],"award-info":[{"award-number":["(Project No. 2022KJCX41"]}]},{"name":"Pediatric Medical Consortium Scientific Research Project of Shanghai Children\u2019s Medical Center affiliated to Shanghai Jiao Tong University School of Medicine"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s11517-025-03347-5","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T03:00:07Z","timestamp":1743735607000},"page":"2693-2706","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An efficient network with state space model under evidential training for fetal echocardiography standard view recognition"],"prefix":"10.1007","volume":"63","author":[{"given":"Changzhao","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiman","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongtong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shibin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxiang","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueping","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"issue":"2","key":"3347_CR1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.ijgo.2016.05.010","volume":"135","author":"KMJ Verdurmen","year":"2016","unstructured":"Verdurmen KMJ, Eijsvoogel NB, Lempersz C et al (2016) A systematic review of prenatal screening for congenital heart disease by fetal electrocardiography. Int J Gynecol Obstet 135(2):129\u2013134","journal-title":"Int J Gynecol Obstet"},{"key":"3347_CR2","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/s00246-006-1291-6","volume":"27","author":"I Germanakis","year":"2006","unstructured":"Germanakis I, Sifakis S (2006) The impact of fetal echocardiography on the prevalence of liveborn congenital heart disease. Pediatr Cardiol 27:465\u2013472","journal-title":"Pediatr Cardiol"},{"issue":"3","key":"3347_CR3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/S2352-4642(19)30402-X","volume":"4","author":"MS Zimmerman","year":"2020","unstructured":"Zimmerman MS, Smith AGC, Sable CA et al (2020) Global, regional, and national burden of congenital heart disease, 1990\u20132017: a systematic analysis for the global burden of disease study 2017. Lancet Child Adolesc Health 4(3):185\u2013200","journal-title":"Lancet Child Adolesc Health"},{"issue":"3","key":"3347_CR4","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.gheart.2018.09.511","volume":"13","author":"H Thomas","year":"2018","unstructured":"Thomas H, Diamond J, Vieco A et al (2018) Global atlas of cardiovascular disease. Glob Heart 13(3):143\u2013163","journal-title":"Glob Heart"},{"key":"3347_CR5","doi-asserted-by":"publisher","unstructured":"Yu X, Ma L, Wang H et al (2024) Deep learning-based differentiation of ventricular septal defect from tetralogy of Fallot in fetal echocardiography images. Technol Health Care. https:\/\/doi.org\/10.3233\/THC-248040. ((Preprint): 1\u20138)","DOI":"10.3233\/THC-248040"},{"key":"3347_CR6","doi-asserted-by":"publisher","first-page":"105280","DOI":"10.1016\/j.bspc.2023.105280","volume":"86","author":"Y Liu","year":"2023","unstructured":"Liu Y, Han X, Liang T et al (2023) EDMAE: an efficient decoupled masked autoencoder for standard view identification in pediatric echocardiography. Biomed Signal Process Control 86:105280","journal-title":"Biomed Signal Process Control"},{"key":"3347_CR7","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.jpeds.2018.08.040","volume":"204","author":"QM Zhao","year":"2019","unstructured":"Zhao QM, Liu F, Wu L et al (2019) Prevalence of congenital heart disease at live birth in China. J Pediatr 204:53\u201358","journal-title":"J Pediatr"},{"issue":"3","key":"3347_CR8","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.echo.2010.12.008","volume":"24","author":"PS Douglas","year":"2011","unstructured":"Douglas PS, Garcia MJ, Haines DE, Lai WW, Manning WJ, Patel AR et al (2011) A report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, American Society of Echocardiography, American Heart Association. J Am Soc Echocardiogr 24(3):229\u2013267","journal-title":"J Am Soc Echocardiogr"},{"issue":"3","key":"3347_CR9","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.jsha.2017.12.001","volume":"30","author":"A Kanwal","year":"2018","unstructured":"Kanwal A, Sheikh AM, Saher T (2018) Determining the factors causing delayed referral for fetal echocardiography at a tertiary care hospital. J Saudi Heart Assoc 30(3):205\u2013210","journal-title":"J Saudi Heart Assoc"},{"key":"3347_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11886-019-1248-z","volume":"21","author":"L Z\u00fchlke","year":"2019","unstructured":"Z\u00fchlke L, Lawrenson J, Comitis G et al (2019) Congenital heart disease in low-and lower-middle\u2013income countries: current status and new opportunities. Curr Cardiol Rep 21:1\u201313","journal-title":"Curr Cardiol Rep"},{"issue":"6","key":"3347_CR11","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.7863\/jum.2013.32.6.1083","volume":"32","author":"JP Crino","year":"2013","unstructured":"Crino JP, Finberg HJ, Frieden F et al (2013) AIUM practice guideline for the performance of obstetric ultrasound examinations. J Ultrasound Med 32(6):1083\u20131101","journal-title":"J Ultrasound Med"},{"key":"3347_CR12","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1002\/uog.12403","volume":"41","author":"JS Carvalho","year":"2013","unstructured":"Carvalho JS, Allan LD, Chaoui R et al (2013) ISUOG Practice Guidelines (updated): sonographic screening examination of the fetal heart. Ultrasound Obstet Gynecol 41:348\u2013359. https:\/\/doi.org\/10.1002\/uog.12403","journal-title":"Ultrasound Obstet Gynecol"},{"issue":"7","key":"3347_CR13","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.echo.2004.04.011","volume":"17","author":"J Rychik","year":"2004","unstructured":"Rychik J, Ayres N, Cuneo B et al (2004) American Society of Echocardiography guidelines and standards for performance of the fetal echocardiogram. J Am Soc Echocardiogr 17(7):803\u2013810","journal-title":"J Am Soc Echocardiogr"},{"issue":"1","key":"3347_CR14","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 XP, Coronado-Guti\u00e9rrez D, Valenzuela-Alcaraz B et al (2020) Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 10(1):10200","journal-title":"Sci Rep"},{"key":"3347_CR15","doi-asserted-by":"publisher","first-page":"770182","DOI":"10.3389\/fped.2021.770182","volume":"9","author":"L Wu","year":"2022","unstructured":"Wu L, Dong B, Liu X et al (2022) Standard echocardiographic view recognition in diagnosis of congenital heart defects in children using deep learning based on knowledge distillation. Front Pediatr 9:770182","journal-title":"Front Pediatr"},{"key":"3347_CR16","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/RBME.2014.2319854","volume":"8","author":"V Sudarshan","year":"2014","unstructured":"Sudarshan V, Acharya UR, Ng EYK, Meng CS, San Tan R, Ghista DN (2014) Automated identification of infarcted myocardium tissue characterization using ultrasound images: a review. IEEE Rev Biomed Eng 8:86\u201397","journal-title":"IEEE Rev Biomed Eng"},{"issue":"12","key":"3347_CR17","doi-asserted-by":"publisher","first-page":"2605","DOI":"10.1109\/TUFFC.2020.2995118","volume":"67","author":"TS Jahren","year":"2020","unstructured":"Jahren TS, Steen EN, Aase SA, Solberg AHS (2020) Estimation of end-diastole in cardiac spectral Doppler using deep learning. IEEE Trans Ultrason Ferroelectr Freq Control 67(12):2605\u20132614","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"issue":"25","key":"3347_CR18","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1093\/eurheartj\/ehz056","volume":"40","author":"C Krittanawong","year":"2019","unstructured":"Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U et al (2019) Deep learning for cardiovascular medicine: a practical primer. Eur Heart J 40(25):2058\u20132073","journal-title":"Eur Heart J"},{"key":"3347_CR19","doi-asserted-by":"publisher","first-page":"648877","DOI":"10.3389\/fcvm.2021.648877","volume":"8","author":"MJ Schuuring","year":"2021","unstructured":"Schuuring MJ, I\u0161gum I, Cosyns B, Chamuleau SA, Bouma BJ (2021) Routine echocardiography and artificial intelligence solutions. Front Cardiovasc Med 8:648877","journal-title":"Front Cardiovasc Med"},{"issue":"4","key":"3347_CR20","doi-asserted-by":"publisher","first-page":"404","DOI":"10.5603\/CJ.a2020.0087","volume":"27","author":"Q Long","year":"2020","unstructured":"Long Q, Ye X, Zhao Q (2020) Artificial intelligence and automation in valvular heart diseases. Cardiol J 27(4):404\u2013420","journal-title":"Cardiol J"},{"key":"3347_CR21","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fcvm.2020.00025","volume":"7","author":"C Chen","year":"2020","unstructured":"Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W et al (2020) Deep learning for cardiac image segmentation: a review. Front Cardiovasc Med 7:25","journal-title":"Front Cardiovasc Med"},{"issue":"6","key":"3347_CR22","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","volume":"6","author":"A Narang","year":"2021","unstructured":"Narang A, Bae R, Hong H, Thomas Y, Surette S, Cadieu C et al (2021) Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use. JAMA cardiology 6(6):624\u2013632","journal-title":"JAMA cardiology"},{"issue":"5","key":"3347_CR23","first-page":"857","volume":"1","author":"M Blaivas","year":"2020","unstructured":"Blaivas M, Adhikari S, Savitsky EA, Blaivas LN, Liu YT (2020) Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between POCUS experts and a deep learning algorithm. J Am Coll Emerg PhysOpen 1(5):857\u2013864","journal-title":"J Am Coll Emerg PhysOpen"},{"issue":"1","key":"3347_CR24","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41746-019-0216-8","volume":"3","author":"A Ghorbani","year":"2020","unstructured":"Ghorbani A, Ouyang D, Abid A et al (2020) Deep learning interpretation of echocardiograms. NPJ digital medicine 3(1):10","journal-title":"NPJ digital medicine"},{"issue":"4","key":"3347_CR25","doi-asserted-by":"publisher","first-page":"044503","DOI":"10.1117\/1.JMI.5.4.044503","volume":"5","author":"P Zhu","year":"2018","unstructured":"Zhu P, Li Z (2018) Guideline-based learning for standard plane extraction in 3-D echocardiography. J Med Imaging 5(4):044503\u2013044503","journal-title":"J Med Imaging"},{"issue":"1","key":"3347_CR26","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/TUFFC.2016.2639071","volume":"64","author":"MC Hemmsen","year":"2016","unstructured":"Hemmsen MC, Lange T, Brandt AH et al (2016) A methodology for anatomic ultrasound image diagnostic quality assessment. IEEE Trans Ultrason Ferroelectr Freq Control 64(1):206\u2013217","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"key":"3347_CR27","doi-asserted-by":"publisher","unstructured":"Van Woudenberg N, Liao Z, Abdi A H et al (2018) Quantitative echocardiography: real-time quality estimation and view classification implemented on a mobile android device[C]\/\/Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation: International Workshops, POCUS 2018, BIVPCS 2018, CuRIOUS 2018, and CPM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16\u201320, 2018, Proceedings. Springer International Publishing, 2018: 74\u201381. https:\/\/doi.org\/10.1007\/978-3-030-01045-4_9","DOI":"10.1007\/978-3-030-01045-4_9"},{"issue":"6245","key":"3347_CR28","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255\u2013260","journal-title":"Science"},{"issue":"21","key":"3347_CR29","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1016\/j.jacc.2016.08.062","volume":"68","author":"S Narula","year":"2016","unstructured":"Narula S, Shameer K, Salem Omar AM et al (2016) Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol 68(21):2287\u20132295","journal-title":"J Am Coll Cardiol"},{"issue":"4","key":"3347_CR30","first-page":"e007138","volume":"11","author":"S Sanchez-Martinez","year":"2018","unstructured":"Sanchez-Martinez S, Duchateau N, Erdei T et al (2018) Machine learning analysis of left ventricular function to characterize heart failure with preserved ejection fraction. Circulation: cardiovascular imaging 11(4):e007138","journal-title":"Circulation: cardiovascular imaging"},{"issue":"7553","key":"3347_CR31","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"3347_CR32","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"3347_CR33","unstructured":"Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems, pp  6000\u20136010"},{"key":"3347_CR34","doi-asserted-by":"publisher","unstructured":"Gu A, Goel K, R\u00e9 C (2021) Efficiently modeling long sequences with structured state spaces. arXiv preprint  arXiv:2111.00396.\u00a0https:\/\/doi.org\/10.48550\/arXiv.2111.00396","DOI":"10.48550\/arXiv.2111.00396"},{"key":"3347_CR35","unstructured":"Gu A, Dao T (2023) Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint  arXiv:2312.00752"},{"issue":"2_Part_1","key":"3347_CR36","first-page":"374","volume":"13","author":"K Kusunose","year":"2020","unstructured":"Kusunose K, Abe T, Haga A, Fukuda D, Yamada H, Harada M et al (2020) A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images. Cardiovascular Imaging 13(2_Part_1):374\u2013381","journal-title":"Cardiovascular Imaging"},{"issue":"4","key":"3347_CR37","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1109\/TMI.2019.2946059","volume":"39","author":"Y Gong","year":"2019","unstructured":"Gong Y, Zhang Y, Zhu H et al (2019) Fetal congenital heart disease echocardiogram screening based on DGACNN: adversarial one-class classification combined with video transfer learning. IEEE Trans Med Imaging 39(4):1206\u20131222","journal-title":"IEEE Trans Med Imaging"},{"key":"3347_CR38","doi-asserted-by":"publisher","unstructured":"Liu Y, Huang Q, Han X et al (2024) Atrial septal defect detection in children based on ultrasound video using multiple instances learning . J Imaging Inform Med 1\u201311.  https:\/\/doi.org\/10.1007\/s10278-024-00987-1","DOI":"10.1007\/s10278-024-00987-1"},{"issue":"3","key":"3347_CR39","doi-asserted-by":"publisher","first-page":"e23086","DOI":"10.1002\/ima.23086","volume":"34","author":"Y Liu","year":"2024","unstructured":"Liu Y, Han X, Liang T et al (2024) Intelligent detection of left ventricular hypertrophy from pediatric echocardiography videos. Int J Imaging Syst Technol 34(3):e23086","journal-title":"Int J Imaging Syst Technol"},{"issue":"16","key":"3347_CR40","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","volume":"138","author":"J Zhang","year":"2018","unstructured":"Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L et al (2018) Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation 138(16):1623\u20131635","journal-title":"Circulation"},{"issue":"10","key":"3347_CR41","doi-asserted-by":"publisher","first-page":"4814","DOI":"10.1109\/JBHI.2021.3091579","volume":"26","author":"S Qiao","year":"2021","unstructured":"Qiao S, Pang S, Luo G et al (2021) FLDS: An intelligent feature learning detection system for visualizing medical images supporting fetal four-chamber views. IEEE J Biomed Health Inform 26(10):4814\u20134825","journal-title":"IEEE J Biomed Health Inform"},{"issue":"21","key":"3347_CR42","doi-asserted-by":"publisher","first-page":"6454","DOI":"10.3390\/jcm11216454","volume":"11","author":"S Nurmaini","year":"2022","unstructured":"Nurmaini S, Partan RU, Bernolian N et al (2022) Deep learning for improving the effectiveness of routine prenatal screening for major congenital heart diseases. J Clin Med 11(21):6454","journal-title":"J Clin Med"},{"key":"3347_CR43","doi-asserted-by":"publisher","unstructured":"Liu Y, Tian Y, Zhao Y et al (2024) VMamba: visual state space model. arXiv preprint arXiv: 2401.10166. https:\/\/doi.org\/10.48550\/arXiv.2401.10166","DOI":"10.48550\/arXiv.2401.10166"},{"key":"3347_CR44","doi-asserted-by":"publisher","unstructured":"Gu A, Dao T (2023) Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint  arXiv:2312.00752. https:\/\/doi.org\/10.48550\/arXiv.2312.0075","DOI":"10.48550\/arXiv.2312.0075"},{"key":"3347_CR45","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L et al (2017) Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition 4700\u20134708. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"3347_CR46","doi-asserted-by":"publisher","unstructured":"Howard A G, Zhu M, Chen B et al (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint  arXiv:1704.04861. https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"3347_CR47","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu CY et al (2022) A ConvNet for the 2020s. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1976\u201311986.  https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"3347_CR48","doi-asserted-by":"publisher","unstructured":"Jsang A (2018) Subjective logic: a formalism for reasoning under uncertainty. Springer Publishing Company, Incorporated. https:\/\/doi.org\/10.1007\/978-3-319-42337-1_17","DOI":"10.1007\/978-3-319-42337-1_17"},{"key":"3347_CR49","unstructured":"Sensoy M, Kaplan L, Kandemir M (2018) Evidential deep learning to quantify classification uncertainty. In: Proceedings of the 32nd International Conference on Neural Inform Process Syst 31. https:\/\/dl.acm.org\/doi\/10.5555\/3327144.3327239. Accessed 3 Dec 2018"},{"key":"3347_CR50","unstructured":"Tan M, Le Q (2021) EfficientNetv2: smaller models and faster training[C]\/\/International conference on machine learning. PMLR 10096\u201310106."},{"key":"3347_CR51","doi-asserted-by":"publisher","unstructured":"Howard A, Sandler M, Chu G et al (2019) Searching for mobileNetv3. Proceedings of the IEEE\/CVF international conference on computer vision 1314\u20131324. https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"key":"3347_CR52","doi-asserted-by":"crossref","unstructured":"Ma N, Zhang X, Zheng HT et al (2018) ShuffleNet v2: practical guidelines for efficient CNN architecture design. Proceedings of the European conference on computer vision (ECCV), pp 116\u2013131","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"3347_CR53","doi-asserted-by":"publisher","unstructured":"Lasala A, Fiorentino MC, Bandini A et al (2024) FetalBrainAwareNet: bridging GANs with anatomical insight for fetal ultrasound brain plane synthesis. Comput Med Imaging Graph 102405. https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102405","DOI":"10.1016\/j.compmedimag.2024.102405"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03347-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03347-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03347-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T08:49:35Z","timestamp":1757148575000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03347-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,2]]},"references-count":53,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["3347"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03347-5","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,2]]},"assertion":[{"value":"6 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests\/personal relationships which may be considered as potential competing interests: Changzhao Chen reports financial support was provided by Hainan Province Clinical Medical Center. Yuqi Zhang report financial support was provided by Sanya Science and Technology Innovation Program. Yiman Liu report financial support was provided by Fundamental Research Funds for the Central Universities, and Pediatric Medical Consortium scientific research project of Shanghai Children\u2019s Medical Center affiliated with Shanghai Jiao Tong University School of Medicine.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}