{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T18:05:52Z","timestamp":1774634752414,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T00:00:00Z","timestamp":1713398400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T00:00:00Z","timestamp":1713398400000},"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":["41831072"],"award-info":[{"award-number":["41831072"]}],"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":["62076190"],"award-info":[{"award-number":["62076190"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017591","name":"Key Industry Innovation Chain of Shaanxi","doi-asserted-by":"publisher","award":["2022ZDLGY01-11"],"award-info":[{"award-number":["2022ZDLGY01-11"]}],"id":[{"id":"10.13039\/501100017591","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Key Industry chain technology research project of Xi\u2019an","award":["23ZDCYJSGG0022-2023"],"award-info":[{"award-number":["23ZDCYJSGG0022-2023"]}]},{"name":"The Youth Open Project of National Space Science Data Center","award":["NSSDC2302005"],"award-info":[{"award-number":["NSSDC2302005"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18985-4","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T10:18:58Z","timestamp":1713435538000},"page":"1907-1925","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["PTC-CapsNet: capsule network for papillary thyroid carcinoma pathological images classification"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6473-0438","authenticated-orcid":false,"given":"Bing","family":"Han","sequence":"first","affiliation":[]},{"given":"Yiyuan","family":"Han","sequence":"additional","affiliation":[]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xinbo","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,18]]},"reference":[{"issue":"3","key":"18985_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209\u2013249","journal-title":"CA Cancer J Clin"},{"key":"18985_CR2","unstructured":"American Cancer Society ( 2021) Cancer facts & figures 2021. American Cancer Society, Atlanta"},{"issue":"6","key":"18985_CR3","doi-asserted-by":"publisher","first-page":"208759","DOI":"10.1001\/jamanetworkopen.2020.8759","volume":"3","author":"Y Deng","year":"2020","unstructured":"Deng Y, Li H, Wang M et al (2020) Global burden of thyroid cancer from 1990 to 2017. JAMA Network Open 3(6):208759\u2013208759","journal-title":"JAMA Network Open"},{"issue":"3","key":"18985_CR4","doi-asserted-by":"publisher","first-page":"2187","DOI":"10.1002\/ijc.29251","volume":"136","author":"C La Vecchia","year":"2015","unstructured":"La Vecchia C, Malvezzi M, Bosetti C, Garavello W, Bertuccio P, Levi F, Negri E (2015) Thyroid cancer mortality and incidence: a global overview. Int J Cancer 136(3):2187\u20132195","journal-title":"Int J Cancer"},{"issue":"8","key":"18985_CR5","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1002\/hed.25740","volume":"41","author":"T Tsujikawa","year":"2019","unstructured":"Tsujikawa T, Thibault G, Azimi V, Sivagnanam S, Banik G, Means C, Kawashima R, Clayburgh DR, Gray JW, Coussens LM, Chang YH (2019) Tumor immune microenvironment characteristics of papillary thyroid carcinoma are associated with histopathological aggressiveness and BRAF mutation status. Head Neck 41(8):2636\u20132646","journal-title":"Head Neck"},{"issue":"6","key":"18985_CR6","first-page":"585","volume":"38","author":"SR Hamilton","year":"2010","unstructured":"Hamilton SR, Aaltonen LA (2010) Pathology and genetics of tumours of the digestive system. Histopathology 38(6):585\u2013585","journal-title":"Histopathology"},{"key":"18985_CR7","volume-title":"Rubin\u2019s Pathology","author":"R Rubin","year":"2007","unstructured":"Rubin R (2007) Rubin\u2019s Pathology. Clinicopathologic Foundations of Medicine. Lippincott Williams & Wilkins, Philadelphia"},{"issue":"2","key":"18985_CR8","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.cmpb.2013.12.012","volume":"113","author":"T Chankong","year":"2014","unstructured":"Chankong T, Theera-Umpon N, Auephanwiriyakul S (2014) Automatic cervical cell segmentation and classification in Pap smears. Comput Methods Prog Biomed 113(2):539\u2013556","journal-title":"Comput Methods Prog Biomed"},{"issue":"6","key":"18985_CR9","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.1109\/JBHI.2015.2483318","volume":"20","author":"P Guo","year":"2016","unstructured":"Guo P, Banerjee K, Joe Stanley R, Long R, Antani S, Thoma G, Zuna R, Frazier SR, Moss RH, Stoecker WV (2016) Nuclei-based features for uterine cervical cancer histology image analysis with fusion-based classification. IEEE J Biomed Health Informat 20(6):1595\u20131607","journal-title":"IEEE J Biomed Health Informat"},{"key":"18985_CR10","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","volume":"33","author":"M Anant","year":"2016","unstructured":"Anant M, George L (2016) Image analysis and machine learning in digital pathology: challenges and opportunities. Med Image Anal 33:170\u2013175","journal-title":"Med Image Anal"},{"key":"18985_CR11","doi-asserted-by":"crossref","unstructured":"Chen H, Han X, Fan X, Lou X, Liu H, Huang J, Yao J (2019) Rectified cross-entropy and upper transition loss for weakly supervised whole slide image classifier. In: International conference on medical image computing and computer-assisted intervention, pp 351\u2013359","DOI":"10.1007\/978-3-030-32239-7_39"},{"issue":"21","key":"18985_CR12","first-page":"15481","volume":"79","author":"G Murtaza","year":"2019","unstructured":"Murtaza G, Shuib L, Mujtaba G, Raza G (2019) Breast cancer multi-classification through deep neural network and hierarchical classification approach. Multimed Tools Appl 79(21):15481\u201315511","journal-title":"Multimed Tools Appl"},{"issue":"15","key":"18985_CR13","doi-asserted-by":"publisher","first-page":"21325","DOI":"10.1007\/s11042-019-7468-9","volume":"78","author":"C Yu","year":"2019","unstructured":"Yu C, Chen H, Li Y, Peng Y, Li J, Yang F (2019) Breast cancer classification in pathological images based on hybrid features. Multimed Tools Appl 78(15):21325\u201321345","journal-title":"Multimed Tools Appl"},{"key":"18985_CR14","doi-asserted-by":"crossref","unstructured":"Bayramoglu N, Kannala J, Heikkil J (2016) Deep learning for magnification independent breast cancer histopathology image classification. In: 2016 23rd international conference on pattern recognition (ICPR), pp 2440\u20132445","DOI":"10.1109\/ICPR.2016.7900002"},{"key":"18985_CR15","doi-asserted-by":"crossref","unstructured":"Hou L, Samaras D, Kurc TM, Gao Y, Davis JE, Saltz JH (2016) Patch-based convolutional neural network for whole slide tissue image classification. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2424\u20132433","DOI":"10.1109\/CVPR.2016.266"},{"key":"18985_CR16","doi-asserted-by":"crossref","unstructured":"Zhu X, Yao J, Zhu F, Huang J (2017) WSISA: making survival prediction from whole slide histopathological images. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6855\u20136863","DOI":"10.1109\/CVPR.2017.725"},{"key":"18985_CR17","doi-asserted-by":"crossref","unstructured":"Li M, Wu L, Wiliem A, Zhao K, Zhang T, Lovell BC (2019) Deep instance-level hard negative mining model for histopathology images. In: International conference on medical image computing and computer-assisted intervention, pp 514\u2013522","DOI":"10.1007\/978-3-030-32239-7_57"},{"issue":"5","key":"18985_CR18","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1109\/TMI.2019.2948026","volume":"39","author":"H Yang","year":"2020","unstructured":"Yang H, Kim J-Y, Kim H, Adhikari SP (2020) Guided soft attention network for classification of breast cancer histopathology images. IEEE Trans Med Imaging 39(5):1306\u20131315","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"18985_CR19","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TMI.2017.2758580","volume":"37","author":"C Mercan","year":"2018","unstructured":"Mercan C, Aksoy S, Mercan E, Shapiro LG, Weaver DL, Elmore JG (2018) Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images. IEEE Trans Med Imaging 37(1):316\u2013325","journal-title":"IEEE Trans Med Imaging"},{"key":"18985_CR20","doi-asserted-by":"publisher","first-page":"105534","DOI":"10.1016\/j.bspc.2023.105534","volume":"87","author":"X Huo","year":"2024","unstructured":"Huo X, Sun G, Tian S, Wang Y, Yu L, Long J, Zhang W, Li A (2024) HiFuse: hierarchical multi-scale feature fusion network for medical image classification. Biomed Signal Process Control 87:105534","journal-title":"Biomed Signal Process Control"},{"key":"18985_CR21","doi-asserted-by":"publisher","first-page":"35977","DOI":"10.1109\/ACCESS.2022.3163822","volume":"10","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Zhang C, Gao S (2022) Breast cancer classification from histopathological images using resolution adaptive network. IEEE Access 10:35977\u201335991","journal-title":"IEEE Access"},{"issue":"1","key":"18985_CR22","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1007\/s11227-020-03321-y","volume":"77","author":"KC Burcak","year":"2021","unstructured":"Burcak KC, Baykan OK, Uguz H (2021) New deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimization of the proposed model. J Supercomput 77(1):973\u2013989","journal-title":"J Supercomput"},{"issue":"3","key":"18985_CR23","first-page":"876","volume":"22","author":"M Alruwaili","year":"2022","unstructured":"Alruwaili M, Gouda W (2022) Automated breast cancer detection models based on transfer learning. J Supercomput 22(3):876","journal-title":"J Supercomput"},{"key":"18985_CR24","doi-asserted-by":"crossref","unstructured":"Arooj S, Atta-ur-Rahman, Zubair M, Khan MF, Alissa K, Khan MA, Mosavi A (2022) Breast cancer detection and classification empowered with transfer learning. Front Public Health 10:924432","DOI":"10.3389\/fpubh.2022.924432"},{"issue":"1","key":"18985_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42484-021-00057-7","volume":"4","author":"A Vanda","year":"2022","unstructured":"Vanda A, Carla S (2022) Quantum transfer learning for breast cancer detection. Quant Mach Intell 4(1):1\u20135","journal-title":"Quant Mach Intell"},{"issue":"1","key":"18985_CR26","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1109\/TCBB.2022.3163277","volume":"20","author":"M Saini","year":"2023","unstructured":"Saini M, Susan S (2023) VGGIN-Net: deep transfer network for imbalanced breast cancer dataset. IEEE\/ACM Trans Comput Biol Bioinforma 20(1):752\u2013762","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"key":"18985_CR27","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"18985_CR28","doi-asserted-by":"crossref","unstructured":"Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classification via capsule networks. In: 2018 25th IEEE international conference on image processing (ICIP), pp 3129\u20133133","DOI":"10.1109\/ICIP.2018.8451379"},{"issue":"1","key":"18985_CR29","doi-asserted-by":"publisher","first-page":"11383","DOI":"10.1038\/s41598-020-68453-w","volume":"10","author":"W Huang","year":"2020","unstructured":"Huang W, Zhou F (2020) DA-CapsNet: dual attention mechanism capsule network. Sci Rep 10(1):11383\u201311383","journal-title":"Sci Rep"},{"key":"18985_CR30","first-page":"3856","volume":"30","author":"S Sabour","year":"2017","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst 30:3856\u20133866","journal-title":"Adv Neural Inf Process Syst"},{"key":"18985_CR31","doi-asserted-by":"crossref","unstructured":"Mobiny A, Nguyen HV (2018) Fast CapsNet for lung cancer screening. In: International conference on medical image computing and computer-assisted intervention, pp 741\u2013749","DOI":"10.1007\/978-3-030-00934-2_82"},{"key":"18985_CR32","doi-asserted-by":"crossref","unstructured":"Afshar P, Plataniotis KN, Mohammadi A (2019) Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. In: ICASSP 2019 - 2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1368\u20131372","DOI":"10.1109\/ICASSP.2019.8683759"},{"issue":"C","key":"18985_CR33","first-page":"110","volume":"140","author":"S Toraman","year":"2020","unstructured":"Toraman S, Alakus TB, Turkoglu I (2020) Convolutional capsnet: a novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos, Solitons Fractals 140(C):110\u2013122","journal-title":"Chaos, Solitons Fractals"},{"issue":"4","key":"18985_CR34","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2018","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2018) Deeplab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18985_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-61068-4","volume-title":"Neural networks: a systematical introduction","author":"R Rojas","year":"1996","unstructured":"Rojas R (1996) Neural networks: a systematical introduction. Springer, Berlin"},{"issue":"4","key":"18985_CR36","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1109\/TMI.2018.2876510","volume":"38","author":"Y Xie","year":"2019","unstructured":"Xie Y, Xia Y, Zhang J, Song Y, Feng D, Fulham M, Cai W (2019) Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Trans Med Imaging 38(4):991\u20131004","journal-title":"IEEE Trans Med Imaging"},{"key":"18985_CR37","volume-title":"Eye movements and vision","author":"AL Yarbus","year":"2013","unstructured":"Yarbus AL (2013) Eye movements and vision. Springer, Berlin"},{"issue":"1","key":"18985_CR38","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"PT De Boer","year":"2005","unstructured":"De Boer PT, Kroese DP, Mannor S, Rubinstein RY (2005) A tutorial on the cross-entropy method. Ann Oper Res 134(1):19\u201367","journal-title":"Ann Oper Res"},{"key":"18985_CR39","unstructured":"Tang Y (2013) Deep learning using linear support vector machines. In: International conference on machine learning"},{"key":"18985_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"18985_CR41","unstructured":"Simonyan K, Zisserman A (2016) Very deep convolutional networks for large-scale image recognition. In: International conference of learning representation, pp 115\u2013121"},{"key":"18985_CR42","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"18985_CR43","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"18985_CR44","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"key":"18985_CR45","doi-asserted-by":"crossref","unstructured":"Xie J, Liu R, Luttrell J, Zhang C (2019) Deep learning based analysis of histopathological images of breast cancer. Front Genet 10(80)","DOI":"10.3389\/fgene.2019.00080"},{"issue":"7","key":"18985_CR46","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","volume":"63","author":"FA Spanhol","year":"2016","unstructured":"Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455\u20131462","journal-title":"IEEE Trans Biomed Eng"},{"key":"18985_CR47","doi-asserted-by":"crossref","unstructured":"Du B, Qi Q, Zheng H et al (2018) Breast cancer histopathological image classification via deep active learning and confidence boosting. In: International conference on artificial neural networks (ICANN 2018), pp 109\u2013116","DOI":"10.1007\/978-3-030-01421-6_11"},{"key":"18985_CR48","doi-asserted-by":"crossref","unstructured":"Gandomkar Z, Brennan PC, Mello-Thoms C (2018) A framework for distinguishing benign from malignant breast histopathological images using deep residual networks. In: 14th International workshop on breast imaging (IWBI 2018), vol 10718","DOI":"10.1117\/12.2318320"},{"issue":"1","key":"18985_CR49","doi-asserted-by":"publisher","first-page":"4172","DOI":"10.1038\/s41598-017-04075-z","volume":"7","author":"Z Han","year":"2017","unstructured":"Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 7(1):4172\u20134172","journal-title":"Sci Rep"},{"issue":"6","key":"18985_CR50","first-page":"316","volume":"9","author":"M Nawaz","year":"2018","unstructured":"Nawaz M, Sewissy AA, Soliman THA (2018) Multi-class breast cancer classification using deep learning convolutional neural network. Adv Theory Simul 9(6):316\u2013332","journal-title":"Adv Theory Simul"},{"issue":"4","key":"18985_CR51","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","volume":"32","author":"MZ Alom","year":"2019","unstructured":"Alom MZ, Yakopcic C, Taha TM, Asari VK (2019) Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imaging 32(4):605\u2013607","journal-title":"J Digit Imaging"},{"key":"18985_CR52","doi-asserted-by":"crossref","unstructured":"Aloyayri A, Krzyzak A (2020) Breast cancer classification from histopathological images using transfer learning and deep neural networks. In: International conference on artificial intelligence and soft computing, pp 491\u2013502","DOI":"10.1007\/978-3-030-61401-0_45"},{"key":"18985_CR53","first-page":"405","volume":"508","author":"A Kumar","year":"2020","unstructured":"Kumar A, Singh SK, Saxena S, Lakshmanan K, Sangaiah AK, Chauhan H, Shrivastava S, Singh RK (2020) Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer. Inf Sci Int J 508:405\u2013421","journal-title":"Inf Sci Int J"},{"key":"18985_CR54","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV), pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18985-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18985-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18985-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T01:48:26Z","timestamp":1738115306000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18985-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,18]]},"references-count":54,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["18985"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18985-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,18]]},"assertion":[{"value":"1 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}