{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:34:14Z","timestamp":1771461254486,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T00:00:00Z","timestamp":1646611200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T00:00:00Z","timestamp":1646611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Development Program of Guangdong Province","award":["2019B010152001"],"award-info":[{"award-number":["2019B010152001"]}]},{"name":"Key Research and Development Program of Jiangsu","award":["BE2021663"],"award-info":[{"award-number":["BE2021663"]}]},{"name":"Research Fund of Jihua Laboratory","award":["X190171TD190"],"award-info":[{"award-number":["X190171TD190"]}]},{"name":"Special fund for high-tech industrialisation of science and technology cooperation between Jilin Province and Chinese Academy of Sciences","award":["2020SYHZ0025"],"award-info":[{"award-number":["2020SYHZ0025"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81871439"],"award-info":[{"award-number":["81871439"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Province Department of Science and Technology","award":["ZR2020QF019"],"award-info":[{"award-number":["ZR2020QF019"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s11517-022-02523-1","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T21:02:28Z","timestamp":1646686948000},"page":"1211-1222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Convolutional neural network-based automatic classification for incomplete antibody reaction intensity in solid phase anti-human globulin test image"],"prefix":"10.1007","volume":"60","author":[{"given":"KeQing","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ShengBao","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YuJue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HongMei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9906-0596","authenticated-orcid":false,"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"issue":"12","key":"2523_CR1","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1001\/jama.288.12.1499","volume":"288","author":"JLBJ Vincent","year":"2002","unstructured":"Vincent JLBJ, Reinhart K et al (2002) Anemia and blood transfusion in critically ill patients. JAMA 288(12):1499\u20131507. https:\/\/doi.org\/10.1001\/jama.288.12.1499","journal-title":"JAMA"},{"key":"2523_CR2","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/s0272-5231(03)00100-x","volume":"24","author":"RE Drews","year":"2003","unstructured":"Drews RE (2003) Critical issues in hematology: anemia, thrombocytopenia, coagulopathy, and blood product transfusions in critically ill patients. Clin Chest Med 24:607\u2013622. https:\/\/doi.org\/10.1016\/s0272-5231(03)00100-x","journal-title":"Clin Chest Med"},{"issue":"1","key":"2523_CR3","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/j.1751-2824.2009.01211.x","volume":"4","author":"J White","year":"2009","unstructured":"White J (2009) Pre-transfusion testing. ISBT Sci Ser 4(1):37\u201344. https:\/\/doi.org\/10.1111\/j.1751-2824.2009.01211.x","journal-title":"ISBT Sci Ser"},{"issue":"2\u20133","key":"2523_CR4","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/S0966-3274(02)00064-3","volume":"10","author":"A Brand","year":"2002","unstructured":"Brand A (2002) Immunological aspects of blood transfusions. Transpl Immunol 10(2\u20133):183\u2013190","journal-title":"Transpl Immunol"},{"issue":"3","key":"2523_CR5","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1111\/j.1423-0410.1995.tb02598.x","volume":"69","author":"DKH Wilhelm","year":"1995","unstructured":"Wilhelm DKH, Klouche M et al (1995) Impact of allergy screening for blood donors: relationship to nonhemolytic transfusion reactions. Vox Sang 69(3):217\u2013221","journal-title":"Vox Sang"},{"key":"2523_CR6","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.transci.2015.06.003","volume":"53","author":"HM Wang","year":"2015","unstructured":"Wang HM, Chen YZ, Ding SH et al (2015) A new approach to detection of incomplete antibodies using hydrogel chromatography medium. Transfus Apheres Sci 53:337\u2013341. https:\/\/doi.org\/10.1016\/j.transci.2015.06.003","journal-title":"Transfus Apheres Sci"},{"issue":"4","key":"2523_CR7","first-page":"255","volume":"26","author":"RRA Coombs","year":"1945","unstructured":"Coombs RRA, Mourant AE, Race RR (1945) A new test for the detection of weak and incomplete Rh agglutinins. Br J Exp Pathol 26(4):255","journal-title":"Br J Exp Pathol"},{"issue":"3895","key":"2523_CR8","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1038\/153771b0","volume":"153","author":"RR Race","year":"1944","unstructured":"Race RR (1944) An \u201cincomplete\u201d antibody in human serum. Nature 153(3895):771\u20132","journal-title":"Nature"},{"key":"2523_CR9","doi-asserted-by":"publisher","unstructured":"Nedelcu E (2013) Pre-analytical issues and interferences in transfusion medicine tests. In: Accurate Results in the Clinical Laboratory: A Guide to Error Detection and Correction. Elsevier Inc., pp 273\u2013294. https:\/\/doi.org\/10.1016\/B978-0-12-415783-5.00017-7","DOI":"10.1016\/B978-0-12-415783-5.00017-7"},{"key":"2523_CR10","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1046\/j.1537-2995.1990.30290162894.x","volume":"30","author":"Y Lapierre","year":"1990","unstructured":"Lapierre Y, Rigal D, Adam J, Josef D, Meyer F, Greber S, Drot C (1990) The gel test: a new way to detect red cell antigen-antibody reactions. Transfusion 30:109\u2013113. https:\/\/doi.org\/10.1046\/j.1537-2995.1990.30290162894.x","journal-title":"Transfusion"},{"key":"2523_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.4103\/0973-6247.126680","volume":"8","author":"S Shastry","year":"2014","unstructured":"Shastry S, Murugesan M, Bhat S (2014) Mixed field agglutination: unusual causes and serological approach. Asian J Transfus Sci 8:4. https:\/\/doi.org\/10.4103\/0973-6247.126680","journal-title":"Asian J Transfus Sci"},{"issue":"1","key":"2523_CR12","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s12288-011-0098-7","volume":"28","author":"V Alwar","year":"2012","unstructured":"Alwar V, Devi AMS, Sitalakshmi S et al (2012) Evaluation of the use of gel card system for assessment of direct coombs test: weighing the pros and cons. Indian J Hematol Blood Transfus 28(1):15\u201318. https:\/\/doi.org\/10.1007\/s12288-011-0098-7","journal-title":"Indian J Hematol Blood Transfus"},{"key":"2523_CR13","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s11517-010-0685-z","volume":"49","author":"Q Liu","year":"2011","unstructured":"Liu Q, Cheng XN, Fei HX (2011) Effects of micro-magnetic field at the surface of 316L and NiTi alloy on blood compatibility. Med Biol Eng Comput 49:359\u2013364. https:\/\/doi.org\/10.1007\/s11517-010-0685-z 2010\/10\/12","journal-title":"Med Biol Eng Comput"},{"issue":"4","key":"2523_CR14","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1007\/BF02443330","volume":"18","author":"WUV Lemm","year":"1980","unstructured":"Lemm WUV, B\u00fccherl ES (1980) Blood compatibility of polymers: in vitro and in vivo tests. Med Biol Eng Compu 18(4):521\u2013526","journal-title":"Med Biol Eng Compu"},{"issue":"6","key":"2523_CR15","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1093\/ajcp\/82.6.719","volume":"82","author":"FV Plapp","year":"1984","unstructured":"Plapp FV, Sinor LT, Rachel JM et al (1984) A solid phase antibody screen. Am J Clin Pathol 82(6):719\u2013721. https:\/\/doi.org\/10.1093\/ajcp\/82.6.719","journal-title":"Am J Clin Pathol"},{"issue":"3","key":"2523_CR16","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1163\/156855900300109143","volume":"30","author":"SG LA Sandler","year":"2000","unstructured":"LA Sandler SG, Rumsey DH, Novak SC (2000) A solid phase and microtiter plate hemagglutination method for pretransfusion compatibility testing. Haematologia 30(3):149\u2013157. https:\/\/doi.org\/10.1163\/156855900300109143","journal-title":"Haematologia"},{"issue":"1","key":"2523_CR17","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1111\/j.1423-0410.1988.tb04684.x","volume":"55","author":"F Guignier","year":"1988","unstructured":"Guignier F, Domy M, Angue M, Richaud P, Chatelain P (1988) Comparison between a solid-phase low-ionic-strength solution antiglobulin test and conventional low-ionic-strength antiglobulin test: assessment for the screening of antierythrocyte antibodies. Vox Sang 55(1):30\u201334","journal-title":"Vox Sang"},{"key":"2523_CR18","doi-asserted-by":"publisher","first-page":"200","DOI":"10.26574\/maedica.2021.16.2.200","volume":"16","author":"A Sigdel","year":"2021","unstructured":"Sigdel A, Chalise G, Bolideei M et al (2021) Comparison between the manual method of indirect coombs via gel technology and solid phase red cell adherence. Maedica (Bucur) 16:200\u2013206. https:\/\/doi.org\/10.26574\/maedica.2021.16.2.200","journal-title":"Maedica (Bucur)"},{"issue":"8","key":"2523_CR19","doi-asserted-by":"publisher","first-page":"693","DOI":"10.5858\/1999-123-0693-EAIOTG","volume":"123","author":"JC Cate IV","year":"1999","unstructured":"Cate JC IV, Reilly N (1999) Evaluation and implementation of the gel test for indirect antiglobulin testing in a community hospital laboratory. Arch Pathol Lab Med. 123(8):693\u2013697","journal-title":"Arch Pathol Lab Med."},{"key":"2523_CR20","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s11517-016-1542-5","volume":"55","author":"A Aristov","year":"2017","unstructured":"Aristov A, Nosova E (2017) Method of evaluation of process of red blood cell sedimentation based on photometry of droplet samples. Med Biol Eng Comput 55:621\u2013630. https:\/\/doi.org\/10.1007\/s11517-016-1542-5 2016\/07\/13","journal-title":"Med Biol Eng Comput"},{"issue":"5","key":"2523_CR21","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1111\/tme.12631","volume":"29","author":"Y Chen","year":"2019","unstructured":"Chen Y, Wang M, Ding S et al (2019) A new reliable test for crossmatching: microplate hydrogel immunoassay technology. Transfus Med 29(5):344\u2013350. https:\/\/doi.org\/10.1111\/tme.12631","journal-title":"Transfus Med"},{"key":"2523_CR22","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1111\/voxs.12591","volume":"15","author":"L Roman","year":"2020","unstructured":"Roman L, Armstrong B, Smart E (2020) Principles of laboratory techniques. ISBT Sci Ser 15:81\u2013111. https:\/\/doi.org\/10.1111\/voxs.12591","journal-title":"ISBT Sci Ser"},{"key":"2523_CR23","doi-asserted-by":"publisher","first-page":"2","DOI":"10.15212\/bioi-2020-0005","volume":"1","author":"P Er Saw","year":"2020","unstructured":"Er Saw P, Jiang S (2020) The significance of interdisciplinary integration in academic research and application. BIO Integration 1:2\u20135. https:\/\/doi.org\/10.15212\/bioi-2020-0005","journal-title":"BIO Integration"},{"key":"2523_CR24","doi-asserted-by":"publisher","first-page":"130","DOI":"10.15212\/bioi-2020-0017","volume":"1","author":"C Liu","year":"2020","unstructured":"Liu C, Jia D, Liu Z (2020) Artificial intelligence (AI)-aided disease prediction. BIO Integration 1:130\u2013136","journal-title":"BIO Integration"},{"key":"2523_CR25","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1016\/j.cell.2020.04.045","volume":"181","author":"K Zhang","year":"2020","unstructured":"Zhang K, Liu X, Shen J et al (2020) Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181:1423-1433.e1411. https:\/\/doi.org\/10.1016\/j.cell.2020.04.045","journal-title":"Cell"},{"key":"2523_CR26","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neunet.2020.05.003","volume":"128","author":"N Das","year":"2020","unstructured":"Das N, Hussain E, Mahanta LB (2020) Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network. Neural Netw 128:47\u201360. https:\/\/doi.org\/10.1016\/j.neunet.2020.05.003","journal-title":"Neural Netw"},{"key":"2523_CR27","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s11517-020-02291-w","volume":"59","author":"AW Tessema","year":"2021","unstructured":"Tessema AW, Mohammed MA, Simegn GL et al (2021) Quantitative analysis of blood cells from microscopic images using convolutional neural network. Med Biol Eng Comput 59:143\u2013152. https:\/\/doi.org\/10.1007\/s11517-020-02291-w","journal-title":"Med Biol Eng Comput"},{"key":"2523_CR28","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty SP, Hughes DP, Salathe M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419. https:\/\/doi.org\/10.3389\/fpls.2016.01419","journal-title":"Front Plant Sci"},{"issue":"6","key":"2523_CR29","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"2523_CR30","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings"},{"key":"2523_CR31","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2016.90"},{"key":"2523_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2016.308"},{"key":"2523_CR33","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L et al (2017) Densely connected convolutional networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017","DOI":"10.1109\/CVPR.2017.243"},{"key":"2523_CR34","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY et al (2018) CBAM: Convolutional block attention module. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2523_CR35","doi-asserted-by":"publisher","first-page":"2365","DOI":"10.1016\/S0031-3203(01)00227-8","volume":"35","author":"M Demrekler","year":"2002","unstructured":"Demrekler M, Altncay H (2002) Plurality voting-based multiple classifier systems: statistically independent with respect to dependent classifier sets. Pattern Recogn 35:2365\u20132379. https:\/\/doi.org\/10.1016\/S0031-3203(01)00227-8","journal-title":"Pattern Recogn"},{"issue":"2","key":"2523_CR36","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s11063-009-9097-1","volume":"29","author":"X Mu","year":"2009","unstructured":"Mu X, Watta P, Hassoun MH (2009) Analysis of a plurality voting-based combination of classifiers. Neural Process Lett 29(2):89\u2013107. https:\/\/doi.org\/10.1007\/s11063-009-9097-1","journal-title":"Neural Process Lett"},{"key":"2523_CR37","doi-asserted-by":"crossref","unstructured":"\u00d6zg\u00fcr A \u00d6L, G\u00fcng\u00f6r T (2005) Text categorization with class-based and corpus-based keyword selection. International Symposium on Computer and Information Sciences Springer, Berlin, Heidelberg; 606\u2013615","DOI":"10.1007\/11569596_63"},{"key":"2523_CR38","doi-asserted-by":"crossref","unstructured":"Yang Y LX (1999) A re-examination of text categorization methods. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval; 42\u201349","DOI":"10.1145\/312624.312647"},{"key":"2523_CR39","doi-asserted-by":"crossref","unstructured":"Lewis DD, Schapire RE, Callan JP, Papka R (1996) Training algorithms for linear text classifiers. Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval; 298\u2013306","DOI":"10.1145\/243199.243277"},{"key":"2523_CR40","doi-asserted-by":"crossref","unstructured":"Harman DK (1995) Overview of the third text retrieval conference (TREC-3). DIANE Publishing","DOI":"10.6028\/NIST.SP.500-225"},{"issue":"6","key":"2523_CR41","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1109\/TST.2012.6374363","volume":"17","author":"PZY Phoungphol","year":"2012","unstructured":"Phoungphol PZY, Zhao Y (2012) Robust multiclass classification for learning from imbalanced biomedical data. Tsinghua Sci Technol 17(6):619\u2013628","journal-title":"Tsinghua Sci Technol"},{"issue":"6","key":"2523_CR42","first-page":"735","volume":"63","author":"PJ Hardin","year":"1997","unstructured":"Hardin PJ, Shumway JM (1997) Statistical significance and normalized confusion matrices. Photogramm Eng Remote Sensing 63(6):735\u20139","journal-title":"Photogramm Eng Remote Sensing"},{"key":"2523_CR43","unstructured":"Kingma DP, Ba JL (2015) Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings"},{"key":"2523_CR44","unstructured":"Grandini M BE, Visani G (2020) Metrics for multi-class classification: an overview. arXiv preprint arXiv; 2008:05756"},{"key":"2523_CR45","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.lungcan.2020.04.014","volume":"145","author":"X Zhao","year":"2020","unstructured":"Zhao X, Wang X, Xia W et al (2020) A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma. Lung Cancer 145:10\u201317. https:\/\/doi.org\/10.1016\/j.lungcan.2020.04.014","journal-title":"Lung Cancer"},{"issue":"1","key":"2523_CR46","doi-asserted-by":"publisher","first-page":"012055","DOI":"10.1088\/1742-6596\/1229\/1\/012055","volume":"1229","author":"I D\u00fcntsch","year":"2019","unstructured":"D\u00fcntsch I, Gediga G (2019) Confusion matrices and rough set data analysis. J Phys: Conf Ser 1229(1):012055. https:\/\/doi.org\/10.1088\/1742-6596\/1229\/1\/012055","journal-title":"J Phys: Conf Ser"},{"key":"2523_CR47","doi-asserted-by":"publisher","first-page":"7560872","DOI":"10.1155\/2019\/7560872","volume":"2019","author":"R Hu","year":"2019","unstructured":"Hu R, Zhou S, Liu Y et al (2019) Margin-based pareto ensemble pruning: an ensemble pruning algorithm that learns to search optimized ensembles. Comput Intell Neurosci 2019:7560872. https:\/\/doi.org\/10.1155\/2019\/7560872","journal-title":"Comput Intell Neurosci"},{"key":"2523_CR48","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.inffus.2017.09.010","volume":"41","author":"RMO Cruz","year":"2018","unstructured":"Cruz RMO, Sabourin R, Cavalcanti GDC (2018) Dynamic classifier selection: recent advances and perspectives. Information Fusion 41:195\u2013216. https:\/\/doi.org\/10.1016\/j.inffus.2017.09.010","journal-title":"Information Fusion"},{"issue":"8","key":"2523_CR49","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.3724\/SP.J.1016.2011.01399","volume":"34","author":"C-X Zhang","year":"2011","unstructured":"Zhang C-X, Zhang J-S (2011) A survey of selective ensemble learning algorithms. Chin J Comput 34(8):1399\u20131410","journal-title":"Chin J Comput"},{"key":"2523_CR50","doi-asserted-by":"crossref","unstructured":"Y L (2012) New discoveries in balanced ensemble learning. The 2012 International Joint Conference on Neural Networks (IJCNN); IEEE, 2012:1\u20138","DOI":"10.1109\/IJCNN.2012.6252423"},{"key":"2523_CR51","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ecoinf.2016.11.012","volume":"37","author":"J Huang","year":"2017","unstructured":"Huang J, Gao J (2017) An ensemble simulation approach for artificial neural network: an example from chlorophyll a simulation in Lake Poyang, China. Ecol Inform 37:52\u201358. https:\/\/doi.org\/10.1016\/j.ecoinf.2016.11.012","journal-title":"Ecol Inform"},{"key":"2523_CR52","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1002\/qj.210","volume":"134","author":"AP Weigel","year":"2008","unstructured":"Weigel AP, Liniger MA, Appenzeller C (2008) Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Q J R Meteorol Soc 134:241\u2013260. https:\/\/doi.org\/10.1002\/qj.210","journal-title":"Q J R Meteorol Soc"},{"key":"2523_CR53","doi-asserted-by":"publisher","first-page":"2600","DOI":"10.1175\/mwr-d-14-00295.1","volume":"143","author":"JP Hacker","year":"2015","unstructured":"Hacker JP, Lei L (2015) Nudging, ensemble, and nudging ensembles for data assimilation in the presence of model error. Mon Weather Rev 143:2600\u20132610. https:\/\/doi.org\/10.1175\/mwr-d-14-00295.1","journal-title":"Mon Weather Rev"},{"key":"2523_CR54","doi-asserted-by":"publisher","unstructured":"Yoo JH, Kang I-S (2005) Theoretical examination of a multi-model composite for seasonal prediction. Geophys Res Lett 32: n\/a-n\/a. https:\/\/doi.org\/10.1029\/2005gl023513","DOI":"10.1029\/2005gl023513"},{"key":"2523_CR55","unstructured":"Zador Paul LD (2013) Evaluating perturbation impact on key travel models, census statistical disclosure control research project 1. westat"},{"key":"2523_CR56","doi-asserted-by":"publisher","first-page":"103357","DOI":"10.1016\/j.trc.2021.103357","volume":"132","author":"H Wu","year":"2021","unstructured":"Wu H, Levinson D (2021) The ensemble approach to forecasting: a review and synthesis. Transport Res Part C: Emerg Technol 132:103357. https:\/\/doi.org\/10.1016\/j.trc.2021.103357","journal-title":"Transport Res Part C: Emerg Technol"},{"issue":"8","key":"2523_CR57","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TKDE.2004.29","volume":"16","author":"GIZZ Webb","year":"2004","unstructured":"Webb GIZZ (2004) Multistrategy ensemble learning: reducing error by combining ensemble learning techniques. IEEE Trans Knowl Data Eng 16(8):980\u2013991","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"2523_CR58","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1023\/A:1007607513941","volume":"40","author":"TG Dietterich","year":"2000","unstructured":"Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40(2):139\u2013157","journal-title":"Mach Learn"},{"issue":"3","key":"2523_CR59","first-page":"801","volume":"26","author":"B Lee","year":"1998","unstructured":"Lee B (1998) Arcing classifiers. Ann Stat 26(3):801\u2013849","journal-title":"Ann Stat"},{"key":"2523_CR60","unstructured":"Elder SLaJF (1997) Bundling heterogeneous classifiers with advisor perceptrons, tech report 97\u20131, Elder Research, Charlottesville"},{"key":"2523_CR61","first-page":"211","volume":"97","author":"DDTG Margineantu","year":"1997","unstructured":"Margineantu DDTG (1997) Pruning adaptive boosting. ICML 97:211\u2013218","journal-title":"ICML"},{"key":"2523_CR62","doi-asserted-by":"crossref","unstructured":"Kamiya R SK, Hotta K (2019) Ensemble of training models for road and building segmentation. 2019 Digital image computing: techniques and applications (DICTA); IEEE, 2019:1\u20136","DOI":"10.1109\/DICTA47822.2019.8945903"},{"key":"2523_CR63","unstructured":"Lin M CQ, Yan S (2013) Network in network. arXiv preprint arXiv; 1312.4400"},{"key":"2523_CR64","doi-asserted-by":"crossref","unstructured":"Hu J SL, Sun G (2018) Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition; 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2523_CR65","doi-asserted-by":"publisher","first-page":"108365","DOI":"10.1016\/j.patcog.2021.108365","volume":"123","author":"T Zhang","year":"2022","unstructured":"Zhang T, Zhang X (2022) A polarization fusion network with geometric feature embedding for SAR ship classification. Pattern Recogn 123:108365. https:\/\/doi.org\/10.1016\/j.patcog.2021.108365","journal-title":"Pattern Recogn"},{"key":"2523_CR66","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. Proceedings of the IEEE conference on computer vision and pattern recognition; 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"},{"key":"2523_CR67","doi-asserted-by":"crossref","unstructured":"Jarrett K KK, Ranzato MA, LeCun Y (2009) What is the best multi-stage architecture for object recognition? IEEE 12th international conference on computer vision; 2146\u20132153","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"2523_CR68","doi-asserted-by":"crossref","unstructured":"Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. IEEE Conference on computer vision and pattern recognition; 1794\u20131801","DOI":"10.1109\/CVPR.2010.5540018"},{"key":"2523_CR69","doi-asserted-by":"crossref","unstructured":"Serre T WL, Poggio T (2005) Object recognition with features inspired by visual cortex. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05); 2: 994\u20131000","DOI":"10.1109\/CVPR.2005.254"},{"key":"2523_CR70","doi-asserted-by":"publisher","first-page":"116057","DOI":"10.1016\/j.eswa.2021.116057","volume":"188","author":"G Xue","year":"2022","unstructured":"Xue G, Liu S, Ren L et al (2022) Forecasting the subway passenger flow under event occurrences with multivariate disturbances. Exp Syst Appl 188:116057. https:\/\/doi.org\/10.1016\/j.eswa.2021.116057","journal-title":"Exp Syst Appl"},{"key":"2523_CR71","doi-asserted-by":"publisher","first-page":"102577","DOI":"10.1016\/j.jag.2021.102577","volume":"104","author":"F Chen","year":"2021","unstructured":"Chen F, Tsou JY (2021) DRSNet: Novel architecture for small patch and low-resolution remote sensing image scene classification. Int J Appl Earth Observ Geoinform 104:102577. https:\/\/doi.org\/10.1016\/j.jag.2021.102577","journal-title":"Int J Appl Earth Observ Geoinform"},{"key":"2523_CR72","doi-asserted-by":"crossref","unstructured":"Boureau YL, Bach F, LeCun Y, Ponce J (2010) Learning mid-level features for recognition. IEEE computer society conference on computer vision and pattern recognition; 2559\u20132566","DOI":"10.1109\/CVPR.2010.5539963"},{"key":"2523_CR73","doi-asserted-by":"publisher","first-page":"108348","DOI":"10.1016\/j.patcog.2021.108348","volume":"122","author":"L Yang","year":"2022","unstructured":"Yang L, Zhang F, Wang PS-P et al (2022) Multi-scale spatial-spectral fusion based on multi-input fusion calculation and coordinate attention for hyperspectral image classification. Pattern Recogn 122:108348. https:\/\/doi.org\/10.1016\/j.patcog.2021.108348","journal-title":"Pattern Recogn"},{"key":"2523_CR74","doi-asserted-by":"publisher","first-page":"108401","DOI":"10.1016\/j.patcog.2021.108401","volume":"123","author":"W Yu","year":"2022","unstructured":"Yu W, Xu H (2022) Co-attentive multi-task convolutional neural network for facial expression recognition. Pattern Recogn 123:108401. https:\/\/doi.org\/10.1016\/j.patcog.2021.108401","journal-title":"Pattern Recogn"},{"key":"2523_CR75","doi-asserted-by":"crossref","unstructured":"Lee S, Kang Q, Madireddy S, Balaprakash P, Agrawal A, Choudhary A, Liao WK (2019) Improving scalability of parallel CNN training by adjusting mini-batch size at run-time. 2019 IEEE International Conference on Big Data (Big Data); 830\u2013839","DOI":"10.1109\/BigData47090.2019.9006550"},{"key":"2523_CR76","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.icte.2020.04.010","volume":"6","author":"I Kandel","year":"2020","unstructured":"Kandel I, Castelli M (2020) The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express 6:312\u2013315. https:\/\/doi.org\/10.1016\/j.icte.2020.04.010","journal-title":"ICT Express"},{"key":"2523_CR77","doi-asserted-by":"crossref","unstructured":"Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures. Neural networks: Tricks of the trade; 437\u2013478","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"2523_CR78","doi-asserted-by":"crossref","unstructured":"Xu H vGJ, Xiong D, Liu, Q (2020) Dynamically adjusting transformer batch size by monitoring gradient direction change. arXiv preprint arXiv; 2005.02008","DOI":"10.18653\/v1\/2020.acl-main.323"},{"key":"2523_CR79","unstructured":"Sutskever I, Martens J, Dahl G, Hinton G (2013) On the importance of initialization and momentum in deep learning. International conference on machine learning; 1139\u20131147"},{"key":"2523_CR80","doi-asserted-by":"publisher","unstructured":"Radiuk PM (2017) Impact of training set batch size on the performance of convolutional neural networks for diverse datasets. Inf Technol Manag Sci 20. https:\/\/doi.org\/10.1515\/itms-2017-0003","DOI":"10.1515\/itms-2017-0003"},{"key":"2523_CR81","unstructured":"Masters D LC (2018) Revisiting small batch training for deep neural networks. arXiv preprint arXiv; arXiv preprint arXiv"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02523-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02523-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02523-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T11:25:18Z","timestamp":1744197918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02523-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,7]]},"references-count":81,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["2523"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02523-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,7]]},"assertion":[{"value":"26 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2022","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}