{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T23:43:20Z","timestamp":1791589400789,"version":"4.3.4"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1016\/j.compmedimag.2024.102454","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T12:30:11Z","timestamp":1729773011000},"page":"102454","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Active learning based on multi-enhanced views for classification of multiple patterns in lung ultrasound images"],"prefix":"10.1016","volume":"118","author":[{"given":"Yuanlu","family":"Ni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Cong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengqian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5135-0631","authenticated-orcid":false,"given":"Guohui","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjun","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2024.102454_b1","doi-asserted-by":"crossref","unstructured":"Anantrasirichai, N., Allinovi, M., Hayes, W., Achim, A.M., 2016. Automatic B-line detection in paediatric lung ultrasound. In: IEEE International Ultrasonics Symposium. IUS.","DOI":"10.1109\/ULTSYM.2016.7728620"},{"key":"10.1016\/j.compmedimag.2024.102454_b2","doi-asserted-by":"crossref","DOI":"10.1007\/BF00116828","article-title":"Queries and concept learning","author":"Angluin","year":"1988","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compmedimag.2024.102454_b3","series-title":"Asymmetric loss for multi-label classification","author":"Ben-Baruch","year":"2020"},{"issue":"9","key":"10.1016\/j.compmedimag.2024.102454_b4","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","article-title":"Learning multi-label scene classification","volume":"37","author":"Boutell","year":"2004","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.compmedimag.2024.102454_b5","article-title":"Intelligent interpretation of four lung ultrasonographic features with split attention based deep learning model","author":"Chen","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.compmedimag.2024.102454_b6","doi-asserted-by":"crossref","unstructured":"Clare, A., King, R.D., 2001. Knowledge Discovery in Multi-label Phenotype Data. In: European Conference on Principles of Data Mining and Knowledge Discovery.","DOI":"10.1007\/3-540-44794-6_4"},{"key":"10.1016\/j.compmedimag.2024.102454_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109926","article-title":"Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees","volume":"133","author":"Custode","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.compmedimag.2024.102454_b8","first-page":"150","article-title":"Committee-based sampling for training probabilistic classifiers","author":"Dagan","year":"1995","journal-title":"Mach. Learn. Proc."},{"key":"10.1016\/j.compmedimag.2024.102454_b9","article-title":"Lung ultrasound in COVID-19 and post-COVID-19 patients, an evidence-based approach","author":"Demi","year":"2021","journal-title":"J. Ultrasound Med."},{"key":"10.1016\/j.compmedimag.2024.102454_b10","series-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102454_b11","doi-asserted-by":"crossref","first-page":"17581","DOI":"10.1038\/s41598-022-22196-y","article-title":"Automatic deep learning-based consolidation\/collapse classification in lung ultrasound images for COVID-19 induced pneumonia","volume":"12","author":"Durrani","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compmedimag.2024.102454_b12","doi-asserted-by":"crossref","unstructured":"Elisseeff, A., Weston, J., 2001. A Kernel Method for Multi-Labelled Classification. In: Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada].","DOI":"10.7551\/mitpress\/1120.003.0092"},{"issue":"3","key":"10.1016\/j.compmedimag.2024.102454_b13","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1109\/TMI.2021.3117246","article-title":"Integrating domain knowledge into deep networks for lung ultrasound with applications to COVID-19","volume":"41","author":"Frank","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2024.102454_b14","series-title":"Dense pixel-labeling for reverse-transfer and diagnostic learning on lung ultrasound for COVID-19 and pneumonia detection","author":"Gare","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102454_b15","series-title":"Deep convolutional ranking for multilabel image annotation","author":"Gong","year":"2013"},{"key":"10.1016\/j.compmedimag.2024.102454_b16","series-title":"Deep convolutional ranking for multilabel image annotation","author":"Gong","year":"2013"},{"key":"10.1016\/j.compmedimag.2024.102454_b17","doi-asserted-by":"crossref","unstructured":"Gopal, S., Yang, Y., 2010. Multilabel classification with meta-level features. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 315\u2013322.","DOI":"10.1145\/1835449.1835503"},{"key":"10.1016\/j.compmedimag.2024.102454_b18","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.patrec.2017.05.012","article-title":"Human attribute recognition by refining attention heat map","volume":"94","author":"Guo","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.compmedimag.2024.102454_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2023.106994","article-title":"Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level","volume":"132","author":"Khan","year":"2023","journal-title":"Ultrasonics"},{"key":"10.1016\/j.compmedimag.2024.102454_b20","doi-asserted-by":"crossref","DOI":"10.1038\/nature02236","article-title":"Functional genomic hypothesis generation and experimentation by a robot scientist","author":"King","year":"2004","journal-title":"Nature"},{"issue":"6","key":"10.1016\/j.compmedimag.2024.102454_b21","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1109\/TSP.2002.1003062","article-title":"Algorithms for optimal scheduling and management of hidden Markov model sensors","volume":"50","author":"Krishnamurthy","year":"2002","journal-title":"IEEE Trans. Signal Process."},{"key":"10.1016\/j.compmedimag.2024.102454_b22","series-title":"Acm Sigir Forum","first-page":"13","article-title":"A sequential algorithm for training text classifiers: Corrigendum and additional data","volume":"Vol. 29","author":"Lewis","year":"1995"},{"key":"10.1016\/j.compmedimag.2024.102454_b23","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, S., Jiang, S., Huang, Q., 2018. Attentive Recurrent Neural Network for Weak-supervised Multi-label Image Classification. In: 2018 ACM Multimedia Conference.","DOI":"10.1145\/3240508.3240649"},{"key":"10.1016\/j.compmedimag.2024.102454_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2024.107409","article-title":"Knowledge fused latent representation from lung ultrasound examination for COVID-19 pneumonia severity assessment","volume":"143","author":"Li","year":"2024","journal-title":"Ultrasonics"},{"key":"10.1016\/j.compmedimag.2024.102454_b25","doi-asserted-by":"crossref","unstructured":"Liu, B., Ferrari, V., 2017. Active Learning for Human Pose Estimation. In: IEEE International Conference on Computer Vision.","DOI":"10.1109\/ICCV.2017.468"},{"key":"10.1016\/j.compmedimag.2024.102454_b26","series-title":"Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification","author":"Liu","year":"2020"},{"key":"10.1016\/j.compmedimag.2024.102454_b27","series-title":"Iconography : Clinical application of rapid B-line score with lung ultrasonography in differentiating between pulmonary infection and pulmonary infection with acute left ventricular heart failure","author":"Liu","year":"2016"},{"key":"10.1016\/j.compmedimag.2024.102454_b28","series-title":"Multi-label text classification with a mixture model trained by EM","author":"Mccallum","year":"1999"},{"key":"10.1016\/j.compmedimag.2024.102454_b29","doi-asserted-by":"crossref","unstructured":"Qi, G.-J., Hua, X.-S., Rui, Y., Tang, J., Mei, T., Zhang, H.-J., 2007. Correlative multi-label video annotation. In: Proceedings of the 15th ACM International Conference on Multimedia. pp. 17\u201326.","DOI":"10.1145\/1291233.1291245"},{"key":"10.1016\/j.compmedimag.2024.102454_b30","series-title":"A survey of deep active learning","author":"Ren","year":"2020"},{"issue":"6","key":"10.1016\/j.compmedimag.2024.102454_b31","doi-asserted-by":"crossref","first-page":"4118","DOI":"10.1121\/10.0007272","article-title":"Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images","volume":"150","author":"Roshankhah","year":"2021","journal-title":"J. Acoust. Soc. Am."},{"issue":"99","key":"10.1016\/j.compmedimag.2024.102454_b32","article-title":"Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound","volume":"PP","author":"Roy","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.compmedimag.2024.102454_b33","series-title":"ACM","first-page":"705","article-title":"Enhancing multi-label music genre classification through ensemble techniques","author":"Sanden","year":"2011"},{"key":"10.1016\/j.compmedimag.2024.102454_b34","series-title":"BoosTexter: A system for multiclass multi-label text categorization","author":"Schapire","year":"1999"},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102454_b35","doi-asserted-by":"crossref","DOI":"10.1007\/s00134-015-4102-9","article-title":"Lung ultrasound training: curriculum implementation and learning trajectory among respiratory therapists","volume":"42","author":"See","year":"2016","journal-title":"Intensive Care Med."},{"key":"10.1016\/j.compmedimag.2024.102454_b36","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D., 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In: IEEE International Conference on Computer Vision.","DOI":"10.1109\/ICCV.2017.74"},{"key":"10.1016\/j.compmedimag.2024.102454_b37","series-title":"Active Learning Literature Survey","author":"Settles","year":"2010"},{"key":"10.1016\/j.compmedimag.2024.102454_b38","series-title":"Variational Adversarial Active Learning","author":"Sinha","year":"2019"},{"issue":"4","key":"10.1016\/j.compmedimag.2024.102454_b39","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1109\/JBHI.2019.2936151","article-title":"Localizing B-lines in lung ultrasonography by weakly supervised deep learning, in-vivo results","volume":"24","author":"Sloun","year":"2020","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.compmedimag.2024.102454_b40","series-title":"Towards computer aided lung ultrasound imaging for the management of patients affected by COVID-19","author":"Soldati","year":"2021"},{"key":"10.1016\/j.compmedimag.2024.102454_b41","doi-asserted-by":"crossref","unstructured":"Song, Y., Zhang, L., Giles, C.L., 2008. A sparse gaussian processes classification framework for fast tag suggestions. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. pp. 93\u2013102.","DOI":"10.1145\/1458082.1458098"},{"key":"10.1016\/j.compmedimag.2024.102454_b42","doi-asserted-by":"crossref","unstructured":"Tao, X., Li, Y., Lau, R.Y.K., Wang, H., 2012. Unsupervised Multi-label Text Classification Using a World Knowledge Ontology. In: Proceedings of the 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - Volume Part I.","DOI":"10.1007\/978-3-642-30217-6_40"},{"key":"10.1016\/j.compmedimag.2024.102454_b43","series-title":"2022 IEEE Biomedical Circuits and Systems Conference (BioCAS)","first-page":"635","article-title":"Semi-automatic A-line detection and confidence scoring in lung ultrasound","author":"Thiery","year":"2022"},{"key":"10.1016\/j.compmedimag.2024.102454_b44","article-title":"Multilabel classification of music into emotions","author":"Trohidis","year":"2008","journal-title":"Blood"},{"key":"10.1016\/j.compmedimag.2024.102454_b45","series-title":"Parametric mixture models for multi-labeled text","author":"Ueda","year":"2002"},{"key":"10.1016\/j.compmedimag.2024.102454_b46","series-title":"2012 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3162","article-title":"Active learning for semantic segmentation with expected change","author":"Vezhnevets","year":"2012"},{"key":"10.1016\/j.compmedimag.2024.102454_b47","series-title":"Multi-label image recognition by recurrently Discovering Attentional Regions","first-page":"464","author":"Wang","year":"2017"},{"key":"10.1016\/j.compmedimag.2024.102454_b48","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Xu, W., 2016. CNN-RNN: A Unified Framework for Multi-label Image Classification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. CVPR.","DOI":"10.1109\/CVPR.2016.251"},{"key":"10.1016\/j.compmedimag.2024.102454_b49","series-title":"ACM","first-page":"17","article-title":"Automatic image annotation via local multi-label classification","author":"Wang","year":"2008"},{"issue":"1","key":"10.1016\/j.compmedimag.2024.102454_b50","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1002\/mp.15908","article-title":"Automatic detection of A-line in lung ultrasound images using deep learning and image processing","volume":"50","author":"Xing","year":"2023","journal-title":"Med. Phys."},{"key":"10.1016\/j.compmedimag.2024.102454_b51","doi-asserted-by":"crossref","unstructured":"Yoo, D., Kweon, I.S., 2019. Learning loss for active learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 93\u2013102.","DOI":"10.1109\/CVPR.2019.00018"},{"key":"10.1016\/j.compmedimag.2024.102454_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102772","article-title":"Triplet attention and dual-pool contrastive learning for clinic-driven multi-label medical image classification","volume":"86","author":"Zhang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2024.102454_b53","series-title":"ResNeSt: Split-attention networks","author":"Zhang","year":"2020"},{"issue":"10","key":"10.1016\/j.compmedimag.2024.102454_b54","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","article-title":"Multilabel neural networks with applications to functional genomics and text categorization","volume":"18","author":"Zhang","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"8","key":"10.1016\/j.compmedimag.2024.102454_b55","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","article-title":"A review on multi-label learning algorithms","volume":"26","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.compmedimag.2024.102454_b56","article-title":"HCP: A flexible CNN framework for multi-label image classification","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compmedimag.2024.102454_b57","series-title":"Multi-Labelled Classification Using Maximum Entropy Method","author":"Zhu","year":"2005"},{"key":"10.1016\/j.compmedimag.2024.102454_b58","series-title":"Learning Spatial Regularization with Image-Level Supervisions for Multi-label Image Classification","author":"Zhu","year":"2017"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124001319?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611124001319?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T09:31:58Z","timestamp":1741253518000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611124001319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":58,"alternative-id":["S0895611124001319"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102454","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Active learning based on multi-enhanced views for classification of multiple patterns in lung ultrasound images","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2024.102454","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102454"}}