{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:43:43Z","timestamp":1780638223847,"version":"3.54.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Nos. 62272418"],"award-info":[{"award-number":["Nos. 62272418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102058"],"award-info":[{"award-number":["62102058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017577","name":"Basic Public Welfare Research Program of Zhejiang Province","doi-asserted-by":"publisher","award":["No.LGG18E050011"],"award-info":[{"award-number":["No.LGG18E050011"]}],"id":[{"id":"10.13039\/501100017577","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"DOI":"10.1007\/s13755-024-00285-8","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T07:01:31Z","timestamp":1710140491000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Gpmb-yolo: a lightweight model for efficient blood cell detection in medical imaging"],"prefix":"10.1007","volume":"12","author":[{"given":"Chenyang","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donglin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6787-5325","authenticated-orcid":false,"given":"Changjun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengye","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,11]]},"reference":[{"key":"285_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2019.109472","volume":"135","author":"H Kutlu","year":"2020","unstructured":"Kutlu H, Avci E, \u00d6zyurt F. White blood cells detection and classification based on regional convolutional neural networks. Med Hypotheses. 2020;135: 109472.","journal-title":"Med Hypotheses"},{"key":"285_CR2","volume-title":"Blood cells: a practical guide","author":"BJ Bain","year":"2021","unstructured":"Bain BJ. Blood cells: a practical guide. New York: Wiley; 2021."},{"key":"285_CR3","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.mpmed.2013.01.023","volume":"41","author":"T Gordon-Smith","year":"2013","unstructured":"Gordon-Smith T. Structure and function of red and white blood cells. Medicine. 2013;41:193\u20139.","journal-title":"Medicine"},{"key":"285_CR4","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1046\/j.1423-0410.2000.7940191.x","volume":"79","author":"N Hamasaki","year":"2000","unstructured":"Hamasaki N, Yamamoto M. Red blood cell function and blood storage. Vox Sang. 2000;79:191\u20137.","journal-title":"Vox Sang"},{"key":"285_CR5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.blre.2004.05.002","volume":"19","author":"P Harrison","year":"2005","unstructured":"Harrison P. Platelet function analysis. Blood Rev. 2005;19:111\u201323.","journal-title":"Blood Rev"},{"issue":"14","key":"285_CR6","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1056\/NEJM199909303411407","volume":"341","author":"B Lowenberg","year":"1999","unstructured":"Lowenberg B, Downing JR, Burnett A. Acute myeloid leukemia. N Engl J Med. 1999;341(14):1051\u201362.","journal-title":"N Engl J Med"},{"issue":"2","key":"285_CR7","first-page":"263","volume":"101","author":"MJ Cascio","year":"2017","unstructured":"Cascio MJ, DeLoughery TG. Anemia: evaluation and diagnostic tests. Med Clin. 2017;101(2):263\u201384.","journal-title":"Med Clin"},{"issue":"6","key":"285_CR8","first-page":"612","volume":"85","author":"RL Gauer","year":"2012","unstructured":"Gauer RL, Braun MM. Thrombocytopenia. Am Fam Phys. 2012;85(6):612\u201322.","journal-title":"Am Fam Phys"},{"key":"285_CR9","first-page":"30","volume":"1","author":"RL Haden","year":"1939","unstructured":"Haden RL. The origin of the microscope. Ann Med Hist. 1939;1:30.","journal-title":"Ann Med Hist"},{"key":"285_CR10","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1893\/0005-3155(2004)75<78:TIOTM>2.0.CO;2","volume":"75","author":"D Bardell","year":"2004","unstructured":"Bardell D. The invention of the microscope. Bios. 2004;75:78\u201384.","journal-title":"Bios"},{"key":"285_CR11","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/0026-2862(73)90086-1","volume":"6","author":"H Schmid-Sch\u00f6nbein","year":"1973","unstructured":"Schmid-Sch\u00f6nbein H, Gosen JV, Heinich L, Klose HJ, Volger E. A counter-rotating, \u201cRheoscope Chamber\u2019\u2019 for the Study of the microrheology of blood cell aggregation by microscopic observation and microphotometry. Microvasc Res. 1973;6:366\u201376.","journal-title":"Microvasc Res"},{"key":"285_CR12","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1182\/blood.V3.2.175.175","volume":"3","author":"JW Rebuck","year":"1948","unstructured":"Rebuck JW, Woods HL. Electron microscope studies of blood cells. Blood. 1948;3:175\u201391.","journal-title":"Blood"},{"key":"285_CR13","unstructured":"Wang H, Lei Z, Zhang X, Zhou B, Peng J. Machine learning basics. Deep Learn. 2016; 98\u2013164."},{"key":"285_CR14","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255\u201360. https:\/\/doi.org\/10.1126\/science.aaa8415.","journal-title":"Science"},{"key":"285_CR15","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"Z-Q Zhao","year":"2019","unstructured":"Zhao Z-Q, Zheng P, Xu S, Wu X. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst. 2019;30:3212\u201332.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"285_CR16","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SAA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203\u201311.","journal-title":"Nat Methods"},{"key":"285_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s13755-022-00204-9","volume":"11","author":"J Zhang","year":"2023","unstructured":"Zhang J, Zhang Y, Jin Y, et al. MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation. Health Inf Sci Syst. 2023;11:13. https:\/\/doi.org\/10.1007\/s13755-022-00204-9.","journal-title":"Health Inf Sci Syst"},{"key":"285_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106073","volume":"149","author":"RA Dar","year":"2022","unstructured":"Dar RA, Rasool M, Assad A. Breast cancer detection using deep learning: datasets, methods, and challenges ahead. Comput Biol Med. 2022;149: 106073.","journal-title":"Comput Biol Med"},{"issue":"5","key":"285_CR19","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/TBCAS.2019.2929053","volume":"13","author":"H Daoud","year":"2019","unstructured":"Daoud H, Bayoumi MA. Efficient epileptic seizure prediction based on deep learning. IEEE Trans Biomed Circ Syst. 2019;13(5):804\u201313.","journal-title":"IEEE Trans Biomed Circ Syst"},{"key":"285_CR20","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. Deep learning. Nature. 2015;521:436\u201344.","journal-title":"Nature"},{"key":"285_CR21","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS. Deep learning for visual understanding: a review. Neurocomputing. 2016;187:27\u201348.","journal-title":"Neurocomputing"},{"key":"285_CR22","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi, A. You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. pp. 779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"285_CR23","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. pp. 7263\u20137271.","DOI":"10.1109\/CVPR.2017.690"},{"key":"285_CR24","unstructured":"Redmon J, Farhadi A. YOLOv3: an incremental improvement 2018."},{"key":"285_CR25","unstructured":"Bochkovskiy A, Wang C-Y, Liao H-YM. YOLOv4: optimal speed and accuracy of object detection; 2020."},{"key":"285_CR26","unstructured":"Thuan D. Evolution of Yolo Algorithm and Yolov5: the state-of-the-art object detention algorithm; 2021."},{"key":"285_CR27","unstructured":"Li C, Li L, Jiang H, Weng K, Geng Y, Li L, Ke Z, Li Q, Cheng M, Nie W, et al. YOLOv6: a single-stage object detection framework for industrial applications; 2022."},{"key":"285_CR28","doi-asserted-by":"crossref","unstructured":"Wang C-Y, Bochkovskiy A, Liao H-YM. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of IEEE\/CVF conference on computer vision and pattern recognition; 2023. pp. 7464\u20137475.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"285_CR29","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","volume":"199","author":"P Jiang","year":"2022","unstructured":"Jiang P, Ergu D, Liu F, Cai Y, Ma B. A review of Yolo algorithm developments. Procedia Comput Sci. 2022;199:1066\u201373. https:\/\/doi.org\/10.1016\/j.procs.2022.01.135.","journal-title":"Procedia Comput Sci"},{"key":"285_CR30","doi-asserted-by":"publisher","first-page":"13895","DOI":"10.1007\/s00521-021-06029-z","volume":"35","author":"R Gai","year":"2023","unstructured":"Gai R, Chen N, Yuan H. A detection algorithm for Cherry fruits based on the improved YOLO-v4 model. Neural Comput Appl. 2023;35:13895\u2013906. https:\/\/doi.org\/10.1007\/s00521-021-06029-z.","journal-title":"Neural Comput Appl"},{"key":"285_CR31","doi-asserted-by":"crossref","unstructured":"Liu W, Ren G, Yu R, Guo S, Zhu J, Zhang L. Image-adaptive YOLO for object detection in adverse weather conditions. In: Proceedings of the AAAI conference on artificial intelligence, vol. 36; 2022. pp. 1792\u2013800.","DOI":"10.1609\/aaai.v36i2.20072"},{"key":"285_CR32","doi-asserted-by":"crossref","unstructured":"Wu S, Zhang L. Using popular object detection methods for real time forest fire detection. In: Proceedings of the 2018 11th international symposium on computational intelligence and design (ISCID), vol. 01; 2018. pp. 280\u2013284.","DOI":"10.1109\/ISCID.2018.00070"},{"key":"285_CR33","doi-asserted-by":"publisher","unstructured":"Wang S, Luo J, Zhou Q, Ren X, Zhang, Y. A differential diagnose method for dermoscopy images. In: 2023 15th international conference on advanced computational intelligence (ICACI), Seoul, Korea, Republic of, 2023, pp. 1\u20138,.https:\/\/doi.org\/10.1109\/ICACI58115.2023.10146178.","DOI":"10.1109\/ICACI58115.2023.10146178"},{"key":"285_CR34","doi-asserted-by":"crossref","unstructured":"Laroca R, Severo E, Zanlorensi LA, Oliveira LS, Gon\u00e7alves GR, Schwartz WR, Menotti D. A robust real-time automatic license plate recognition based on the YOLO detector. In: Proceedings of the 2018 international joint conference on neural networks (ijcnn); IEEE; 2018. pp. 1\u201310.","DOI":"10.1109\/IJCNN.2018.8489629"},{"key":"285_CR35","doi-asserted-by":"crossref","unstructured":"Kuznetsova A, Maleva T, Soloviev V. Detecting Apples in Orchards Using YOLOv3 and YOLOv5 in General and Close-up Images. In: Proceedings of the advances in neural networks-ISNN 2020: 17th international symposium on neural networks, ISNN 2020, Cairo, Egypt, December 4\u20136, 2020, Proceedings 17; Springer; 2020. pp. 233\u2013243.","DOI":"10.1007\/978-3-030-64221-1_20"},{"key":"285_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113211","volume":"149","author":"PP Banik","year":"2020","unstructured":"Banik PP, Saha R, Kim KD. An automatic nucleus segmentation and CNN model based classification method of white blood cell. Expert Syst Appl. 2020;149: 113211.","journal-title":"Expert Syst Appl"},{"key":"285_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103590","volume":"75","author":"B Leng","year":"2022","unstructured":"Leng B, Leng M, Ge M, et al. Knowledge distillation-based deep learning classification network for peripheral blood leukocytes. Biomed Signal Process Control. 2022;75: 103590.","journal-title":"Biomed Signal Process Control"},{"key":"285_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117672","volume":"205","author":"M Hosseini","year":"2022","unstructured":"Hosseini M, Bani-Hani D, Lam SS. Leukocytes image classification using optimized convolutional neural networks. Expert Syst Appl. 2022;205: 117672.","journal-title":"Expert Syst Appl"},{"key":"285_CR39","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1007\/s11517-016-1590-x","volume":"55","author":"J Zhao","year":"2017","unstructured":"Zhao J, Zhang M, Zhou Z, Chu J, Cao F. Automatic identifying and counting blood cells in smear images. Med Biol Eng Comput. 2017;55:1287\u2013301. https:\/\/doi.org\/10.1007\/s11517-016-1590-x.","journal-title":"Med Biol Eng Comput"},{"key":"285_CR40","doi-asserted-by":"crossref","unstructured":"Raina S, Khandelwal A, Gupta S, et al. Blood cells detection using faster-RCNN. In: 2020 IEEE international conference on computing, power and communication technologies (GUCON). IEEE; 2020. pp. 217\u2013222.","DOI":"10.1109\/GUCON48875.2020.9231134"},{"key":"285_CR41","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1049\/htl.2018.5098","volume":"6","author":"MM Alam","year":"2019","unstructured":"Alam MM, Islam MT. Machine learning approach of automatic identification and counting of blood cells. Healthc Technol Lett. 2019;6:103\u20138. https:\/\/doi.org\/10.1049\/htl.2018.5098.","journal-title":"Healthc. Technol. Lett."},{"key":"285_CR42","doi-asserted-by":"publisher","unstructured":"Rohaziat N, Razali M, Nurshazwani W, Othman N. White blood cells detection using YOLOv3 with CNN feature extraction models. IJACSA; 2020, 11. https:\/\/doi.org\/10.14569\/IJACSA.2020.0111058.","DOI":"10.14569\/IJACSA.2020.0111058"},{"key":"285_CR43","doi-asserted-by":"crossref","unstructured":"Xia T, Fu YQ, Jin N, Chazot P, Angelov P, Jiang R. AI-enabled microscopic blood analysis for microfluidic COVID-19 hematology. In: Proceedings of the 2020 5th international conference on computational intelligence and applications (ICCIA); IEEE; 2020. pp. 98\u2013102.","DOI":"10.1109\/ICCIA49625.2020.00026"},{"key":"285_CR44","doi-asserted-by":"publisher","unstructured":"Liu R, Ren C, Fu M, Chu Z, Guo J. Platelet detection based on improved YOLO_v3. Cyborg Bionic Syst 2022, 2022, 2022\/9780569, https:\/\/doi.org\/10.34133\/2022\/9780569.","DOI":"10.34133\/2022\/9780569"},{"key":"285_CR45","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1186\/s12859-022-05074-2","volume":"22","author":"Y-M Chen","year":"2022","unstructured":"Chen Y-M, Tsai J-T, Ho W-H. Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method. BMC Bioinform. 2022;22:635. https:\/\/doi.org\/10.1186\/s12859-022-05074-2.","journal-title":"BMC Bioinform"},{"key":"285_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102495","volume":"66","author":"A Shakarami","year":"2021","unstructured":"Shakarami A, Menhaj MB, Mahdavi-Hormat A, Tarrah H. A fast and yet efficient YOLOv3 for blood cell detection. Biomed Signal Process Control. 2021;66: 102495. https:\/\/doi.org\/10.1016\/j.bspc.2021.102495.","journal-title":"Biomed Signal Process Control"},{"key":"285_CR47","doi-asserted-by":"crossref","unstructured":"Liu C, Li D, Huang P. ISE-YOLO: improved squeeze-and-excitation attention module based YOLO for blood cells detection. In: Proceedings of the 2021 IEEE international conference on big data (Big Data); IEEE; 2021. pp. 3911\u20133916.","DOI":"10.1109\/BigData52589.2021.9672069"},{"key":"285_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103416","volume":"73","author":"F Xu","year":"2022","unstructured":"Xu F, Li X, Yang H, Wang Y, Xiang W. TE-YOLOF: tiny and efficient YOLOF for blood cell detection. Biomed Signal Process Control. 2022;73: 103416. https:\/\/doi.org\/10.1016\/j.bspc.2021.103416.","journal-title":"Biomed Signal Process Control"},{"key":"285_CR49","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C. GhostNet: more features from cheap operations. In Proceedings of the 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR); 2020. pp. 1577\u20131586.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"285_CR50","doi-asserted-by":"crossref","unstructured":"Chen J, Kao S, He H, et al. Run, don\u2019t walk: chasing higher FLOPS for faster neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2023. pp. 12021\u201312031.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"285_CR51","unstructured":"Yang L, Zhang R Y, Li L, et al. Simam: a simple, parameter-free attention module for convolutional neural networks. In: International conference on machine learning. PMLR; 2021. pp. 11863\u201311874."},{"key":"285_CR52","doi-asserted-by":"crossref","unstructured":"Liu S, Qi L, Qin H, et al. Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2018. pp. 8759\u20138768.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"285_CR53","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV. EfficientDet: scalable and efficient object detection. In: Proceedings of the 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR); 2020. pp. 10778\u201310787.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"285_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107079","volume":"126","author":"Y Yu","year":"2023","unstructured":"Yu Y, Zhang Y, Cheng Z, et al. MCA: multidimensional collaborative attention in deep convolutional neural networks for image recognition. Eng Appl Artif Intell. 2023;126: 107079.","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"285_CR55","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH. Genetic algorithms. Sci Am. 1992;267(1):66\u201373.","journal-title":"Sci Am"},{"key":"285_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121597","volume":"237","author":"D Zhu","year":"2024","unstructured":"Zhu D, Wang S, Zhou C, et al. Human memory optimization algorithm: a memory-inspired optimizer for global optimization problems. Expert Syst Appl. 2024;237: 121597.","journal-title":"Expert Syst Appl"},{"key":"285_CR57","doi-asserted-by":"publisher","first-page":"12363","DOI":"10.1007\/s00521-020-04832-8","volume":"32","author":"A Slowik","year":"2020","unstructured":"Slowik A, Kwasnicka H. Evolutionary algorithms and their applications to engineering problems. Neural Comput Appl. 2020;32:12363\u201379.","journal-title":"Neural Comput Appl"},{"key":"285_CR58","doi-asserted-by":"crossref","unstructured":"Zhu D, Wang S, Zhou C, et al. Manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems. Appl Soft Comput. 2023; 110561.","DOI":"10.1016\/j.asoc.2023.110561"},{"key":"285_CR59","unstructured":"He X, Pan Q, Gao L, et al. A greedy cooperative co-evolution ary algorithm with problem-specific knowledge for multi-objective flowshop group scheduling problem. IEEE Trans Evolut Comput; 2021."},{"key":"285_CR60","unstructured":"Gan, S. BCCD_Dataset, HPC-AI Lab, 2018. https:\/\/github.com\/Shenggan\/BCCD_Dataset."},{"issue":"4","key":"285_CR61","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.compmedimag.2011.01.003","volume":"35","author":"SH Rezatofighi","year":"2011","unstructured":"Rezatofighi SH, Soltanian-Zadeh H. Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Imaging Graph. 2011;35(4):333\u201343.","journal-title":"Comput Med Imaging Graph"},{"key":"285_CR62","doi-asserted-by":"crossref","unstructured":"Chen H, Liu J, Hua C, et al. Transmixnet: an attention based double-branch model for white blood cell classification and its training with the fuzzified training data. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2021. pp. 842\u2013847.","DOI":"10.1109\/BIBM52615.2021.9669587"}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-024-00285-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13755-024-00285-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13755-024-00285-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T08:27:06Z","timestamp":1733214426000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13755-024-00285-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,11]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["285"],"URL":"https:\/\/doi.org\/10.1007\/s13755-024-00285-8","relation":{},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,11]]},"assertion":[{"value":"4 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2024","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"24"}}