{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:44:36Z","timestamp":1774449876770,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning demonstrates impressive performance in many medical image analysis tasks. However, its reliability builds on the labeled medical datasets and the assumption of the same distributions between the training data (source domain) and the test data (target domain). Therefore, some unsupervised medical domain adaptation networks transfer knowledge from the source domain with rich labeled data to the target domain with only unlabeled data by learning domain-invariant features. We observe that conventional adversarial-training-based methods focus on the global distributions alignment and may overlook the class-level information, which will lead to negative transfer. In this paper, we attempt to learn the robust features alignment for the cross-domain medical image analysis. Specifically, in addition to a discriminator for alleviating the domain shift, we further introduce an auxiliary classifier to achieve robust features alignment with the class-level information. We first detect the unreliable target samples, which are far from the source distribution via diverse training between two classifiers. Next, a cross-classifier consistency regularization is proposed to align these unreliable samples and the negative transfer can be avoided. In addition, for fully exploiting the knowledge of unlabeled target data, we further propose a within-classifier consistency regularization to improve the robustness of the classifiers in the target domain, which enhances the unreliable target samples detection as well. We demonstrate that our proposed dual-consistency regularizations achieve state-of-the-art performance on multiple medical adaptation tasks in terms of both accuracy and Macro-F1-measure. Extensive ablation studies and visualization results are also presented to verify the effectiveness of each proposed module. For the skin adaptation results, our method outperforms the baseline and the second-best method by around 10 and 4 percentage points. Similarly, for the COVID-19 adaptation task, our model achieves consistently the best performance in terms of both accuracy (96.93%) and Macro-F1 (86.52%).<\/jats:p>","DOI":"10.1007\/s40747-023-01297-9","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T09:02:15Z","timestamp":1702544535000},"page":"2717-2731","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Learning robust features alignment for cross-domain medical image analysis"],"prefix":"10.1007","volume":"10","author":[{"given":"Zhen","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7204-7030","authenticated-orcid":false,"given":"Cheng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"1297_CR1","doi-asserted-by":"publisher","first-page":"2385","DOI":"10.1109\/TMI.2020.2971258","volume":"39","author":"E Ahn","year":"2020","unstructured":"Ahn E, Kumar A, Fulham MJ, Feng D, Kim J (2020) Unsupervised domain adaptation to classify medical images using zero-bias convolutional auto-encoders and context-based feature augmentation. IEEE Trans Med Imaging 39:2385\u20132394. https:\/\/doi.org\/10.1109\/TMI.2020.2971258","journal-title":"IEEE Trans Med Imaging"},{"key":"1297_CR2","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","volume":"79","author":"S Ben-David","year":"2010","unstructured":"Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vaughan JW (2010) A theory of learning from different domains. Mach Learn 79:151\u2013175","journal-title":"Mach Learn"},{"key":"1297_CR3","unstructured":"Berthelot D, Carlini N, Cubuk ED, Kurakin A, Sohn K, Zhang H, Raffel C (2020) Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring. In: 8th International conference on learning representations, ICLR, OpenReview.net. https:\/\/openreview.net\/forum?id=HklkeR4KPB"},{"key":"1297_CR4","doi-asserted-by":"crossref","unstructured":"Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsupervised pixel-level domain adaptation with generative adversarial networks. In: IEEE conference on computer vision and pattern recognition. pp 95\u2013104","DOI":"10.1109\/CVPR.2017.18"},{"key":"1297_CR5","unstructured":"Bousmalis K, Trigeorgis G, Silberman N, Krishnan D, Erhan D (2016) Domain separation networks. In: Advances in neural information processing systems. pp 343\u2013351"},{"key":"1297_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107192","volume":"227","author":"S Chen","year":"2021","unstructured":"Chen S, Wu H, Liu C (2021) Domain invariant and agnostic adaptation. Knowl Based Syst 227:107192","journal-title":"Knowl Based Syst"},{"key":"1297_CR7","unstructured":"Chen X, Wang S, Long M, Wang J (2019) Transferability vs. discriminability: batch spectral penalization for adversarial domain adaptation. In: International conference on machine learning, PMLR. pp 1081\u20131090"},{"key":"1297_CR8","unstructured":"Cohen JP, Morrison P, Dao L (2020) COVID-19 image data collection. CoRR abs\/2003.11597. arXiv:2003.11597"},{"key":"1297_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102293","volume":"75","author":"D Dai","year":"2022","unstructured":"Dai D, Dong C, Xu S, Yan Q, Li Z, Zhang C, Luo N (2022) Ms RED: a novel multi-scale residual encoding and decoding network for skin lesion segmentation. Med Image Anal 75:102293. https:\/\/doi.org\/10.1016\/j.media.2021.102293","journal-title":"Med Image Anal"},{"key":"1297_CR10","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L, Li K, Li F (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1297_CR11","first-page":"59:1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky VS (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17:59:1-59:35","journal-title":"J Mach Learn Res"},{"key":"1297_CR12","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems. pp 2672\u20132680"},{"key":"1297_CR13","unstructured":"Gretton A, Sriperumbudur BK, Sejdinovic D, Strathmann H, Balakrishnan S, Pontil M, Fukumizu K (2012) Optimal kernel choice for large-scale two-sample tests. In: Advances in neural information processing systems. pp 1214\u20131222"},{"key":"1297_CR14","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1109\/JBHI.2019.2942429","volume":"24","author":"Y Gu","year":"2020","unstructured":"Gu Y, Ge Z, Bonnington CP, Zhou J (2020) Progressive transfer learning and adversarial domain adaptation for cross-domain skin disease classification. IEEE J Biomed Health Inform 24:1379\u20131393. https:\/\/doi.org\/10.1109\/JBHI.2019.2942429","journal-title":"IEEE J Biomed Health Inform"},{"key":"1297_CR15","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2022","unstructured":"Guan H, Liu M (2022) Domain adaptation for medical image analysis: a survey. IEEE Trans Biomed Eng 69:1173\u20131185. https:\/\/doi.org\/10.1109\/TBME.2021.3117407","journal-title":"IEEE Trans Biomed Eng"},{"key":"1297_CR16","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.ins.2020.08.075","volume":"544","author":"C Han","year":"2021","unstructured":"Han C, Lei Y, Xie Y, Zhou D, Gong M (2021) Learning smooth representations with generalized softmax for unsupervised domain adaptation. Inf Sci 544:415\u2013426. https:\/\/doi.org\/10.1016\/j.ins.2020.08.075","journal-title":"Inf Sci"},{"key":"1297_CR17","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition. pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1297_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108035","volume":"118","author":"W Hryniewska","year":"2021","unstructured":"Hryniewska W, Bombinski P, Szatkowski P, Tomaszewska P, Przelaskowski A, Biecek P (2021) Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies. Pattern Recognit 118:108035. https:\/\/doi.org\/10.1016\/j.patcog.2021.108035","journal-title":"Pattern Recognit"},{"key":"1297_CR19","doi-asserted-by":"publisher","unstructured":"Jin Y, Wang X, Long M, Wang J (2020) Minimum class confusion for versatile domain adaptation. In: Vedaldi A, Bischof H, Brox T, Frahm J (eds) Computer vision\u2014ECCV 2020\u201416th European conference. Springer, Berlin, pp 464\u2013480. https:\/\/doi.org\/10.1007\/978-3-030-58589-1_28","DOI":"10.1007\/978-3-030-58589-1_28"},{"key":"1297_CR20","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/JBHI.2016.2635663","volume":"21","author":"A Kumar","year":"2017","unstructured":"Kumar A, Kim J, Lyndon D, Fulham MJ, Feng DD (2017) An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J Biomed Health Inform 21:31\u201340. https:\/\/doi.org\/10.1109\/JBHI.2016.2635663","journal-title":"IEEE J Biomed Health Inform"},{"key":"1297_CR21","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.ins.2022.07.068","volume":"609","author":"J Li","year":"2022","unstructured":"Li J, L\u00fc S, Li Z (2022) Unsupervised domain adaptation via softmax-based prototype construction and adaptation. Inf Sci 609:257\u2013275. https:\/\/doi.org\/10.1016\/j.ins.2022.07.068","journal-title":"Inf Sci"},{"key":"1297_CR22","doi-asserted-by":"publisher","unstructured":"Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M (2014) Medical image classification with convolutional neural network. In: 13th international conference on control automation robotics & vision, ICARCV 2014, Singapore, December 10\u201312, 2014, IEEE. pp 844\u2013848. https:\/\/doi.org\/10.1109\/ICARCV.2014.7064414","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"1297_CR23","doi-asserted-by":"publisher","first-page":"4809","DOI":"10.1007\/s10462-021-10121-0","volume":"55","author":"X Li","year":"2022","unstructured":"Li X, Li C, Rahaman MM, Sun H, Li X, Wu J, Yao Y, Grzegorzek M (2022) A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev 55:4809\u20134878","journal-title":"Artif Intell Rev"},{"key":"1297_CR24","doi-asserted-by":"crossref","unstructured":"Liu C, Wu S, Cao W, Shen W, Jiang D, Yu Z, Wong HS (2020) Joint subspace and discriminative learning for self-paced domain adaptation. Knowl Based Syst 205, 106285","DOI":"10.1016\/j.knosys.2020.106285"},{"key":"1297_CR25","unstructured":"Liu H, Long M, Wang J, Jordan MI (2019) Transferable adversarial training: a general approach to adapting deep classifiers. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th international conference on machine learning, ICML, PMLR. pp 4013\u20134022. http:\/\/proceedings.mlr.press\/v97\/liu19b.html"},{"key":"1297_CR26","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1109\/TMI.2020.2995518","volume":"39","author":"Q Liu","year":"2020","unstructured":"Liu Q, Yu L, Luo L, Dou Q, Heng P (2020) Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans Med Imaging 39:3429\u20133440. https:\/\/doi.org\/10.1109\/TMI.2020.2995518","journal-title":"IEEE Trans Med Imaging"},{"key":"1297_CR27","doi-asserted-by":"publisher","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","volume":"41","author":"M Long","year":"2019","unstructured":"Long M, Cao Y, Cao Z, Wang J, Jordan MI (2019) Transferable representation learning with deep adaptation networks. IEEE Trans Pattern Anal Mach Intell 41:3071\u20133085","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1297_CR28","unstructured":"Long M, Cao Z, Wang J, Jordan MI (2018) Conditional adversarial domain adaptation. In: Advances in neural information processing systems. pp 1647\u20131657"},{"key":"1297_CR29","unstructured":"Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning. pp 2208\u20132217"},{"key":"1297_CR30","unstructured":"Luo Z, Zou Y, Hoffman J, Li F (2017) Label efficient learning of transferable representations acrosss domains and tasks. In: Advances in neural information processing systems. pp 164\u2013176"},{"key":"1297_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101794","volume":"65","author":"S Minaee","year":"2020","unstructured":"Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ (2020) Deep-covid: predicting COVID-19 from chest x-ray images using deep transfer learning. Med Image Anal 65:101794. https:\/\/doi.org\/10.1016\/j.media.2020.101794","journal-title":"Med Image Anal"},{"key":"1297_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.106221","volume":"32","author":"AG Pacheco","year":"2020","unstructured":"Pacheco AG, Lima GR, Salom\u00e3o AS, Krohling B, Biral IP, de Angelo GG, Alves FC Jr, Esgario JG, Simora AC, Castro PB et al (2020) PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Brief 32:106221","journal-title":"Data Brief"},{"key":"1297_CR33","doi-asserted-by":"crossref","unstructured":"Saito K, Watanabe K, Ushiku Y, Harada T (2018) Maximum classifier discrepancy for unsupervised domain adaptation. In: IEEE conference on computer vision and pattern recognition. pp 3723\u20133732","DOI":"10.1109\/CVPR.2018.00392"},{"key":"1297_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102046","volume":"71","author":"A Signoroni","year":"2021","unstructured":"Signoroni A, Savardi M, Benini S, Adami N, Leonardi R, Gibellini P, Vaccher F, Ravanelli M, Borghesi A, Maroldi R, Farina D (2021) Bs-net: learning COVID-19 pneumonia severity on a large chest x-ray dataset. Med Image Anal 71:102046. https:\/\/doi.org\/10.1016\/j.media.2021.102046","journal-title":"Med Image Anal"},{"key":"1297_CR35","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR arXiv:abs\/1409.1556"},{"key":"1297_CR36","unstructured":"Sohn K, Berthelot D, Carlini N, Zhang Z, Zhang H, Raffel C, Cubuk ED, Kurakin A, Li C (2020) Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In: Advances in neural information processing systems"},{"key":"1297_CR37","doi-asserted-by":"crossref","unstructured":"Sun B, Saenko K (2016) Deep coral: correlation alignment for deep domain adaptation. In: Computer vision\u2014ECCV workshops. pp 443\u2013450","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"1297_CR38","doi-asserted-by":"publisher","first-page":"3595","DOI":"10.1109\/JBHI.2020.3037127","volume":"24","author":"S Tabik","year":"2020","unstructured":"Tabik S, G\u00f3mez-R\u00edos A, Mart\u00edn-Rodr\u00edguez JL, Sevillano-Garc\u00eda I, Rey-Area M, Charte D, Guirado E, Su\u00e1rez J, Luengo J, Valero-Gonz\u00e1lez MA, Garc\u00eda-Villanova P, Olmedo-S\u00e1nchez E, Herrera F (2020) COVIDGR dataset and covid-sdnet methodology for predicting COVID-19 based on chest x-ray images. IEEE J Biomed Health Inform 24:3595\u20133605. https:\/\/doi.org\/10.1109\/JBHI.2020.3037127","journal-title":"IEEE J Biomed Health Inform"},{"key":"1297_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108903","volume":"248","author":"Q Tian","year":"2022","unstructured":"Tian Q, Zhou J, Chu Y (2022) Joint bi-adversarial learning for unsupervised domain adaptation. Knowl Based Syst 248:108903. https:\/\/doi.org\/10.1016\/j.knosys.2022.108903","journal-title":"Knowl Based Syst"},{"key":"1297_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl P, Rosendahl C, Kittler H (2018) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5:1\u20139","journal-title":"Sci Data"},{"key":"1297_CR41","doi-asserted-by":"crossref","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: IEEE conference on computer vision and pattern recognition. pp 2962\u20132971","DOI":"10.1109\/CVPR.2017.316"},{"key":"1297_CR42","unstructured":"Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: maximizing for domain invariance. CoRR arXiv:abs\/1412.3474"},{"key":"1297_CR43","doi-asserted-by":"publisher","unstructured":"Xu R, Li G, Yang J, Lin L (2019) Larger norm more transferable: an adaptive feature norm approach for unsupervised domain adaptation. In: 2019 IEEE\/CVF international conference on computer vision, ICCV, IEEE. pp 1426\u20131435. https:\/\/doi.org\/10.1109\/ICCV.2019.00151","DOI":"10.1109\/ICCV.2019.00151"},{"key":"1297_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106394","volume":"207","author":"L Yang","year":"2020","unstructured":"Yang L, Zhong P (2020) Discriminative and informative joint distribution adaptation for unsupervised domain adaptation. Knowl Based Syst 207:106394. https:\/\/doi.org\/10.1016\/j.knosys.2020.106394","journal-title":"Knowl Based Syst"},{"key":"1297_CR45","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Advances in neural information processing systems, pp 3320\u20133328"},{"key":"1297_CR46","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1109\/TMI.2016.2642839","volume":"36","author":"L Yu","year":"2017","unstructured":"Yu L, Chen H, Dou Q, Qin J, Heng P (2017) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 36:994\u20131004. https:\/\/doi.org\/10.1109\/TMI.2016.2642839","journal-title":"IEEE Trans Med Imaging"},{"key":"1297_CR47","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1016\/j.ins.2021.07.073","volume":"575","author":"C Zhang","year":"2021","unstructured":"Zhang C, Zhao Q (2021) Deep discriminative domain adaptation. Inf Sci 575:599\u2013610. https:\/\/doi.org\/10.1016\/j.ins.2021.07.073","journal-title":"Inf Sci"},{"key":"1297_CR48","unstructured":"Zhang Y (2021) A survey of unsupervised domain adaptation for visual recognition. CoRR arXiv:abs\/2112.06745"},{"key":"1297_CR49","unstructured":"Zhang Y, Niu S, Qiu Z, Wei Y, Zhao P, Yao J, Huang J, Wu Q, Tan M (2020) COVID-DA: deep domain adaptation from typical pneumonia to COVID-19. CoRR abs\/2005.01577. arXiv:2005.01577"},{"key":"1297_CR50","doi-asserted-by":"publisher","first-page":"7834","DOI":"10.1109\/TIP.2020.3006377","volume":"29","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wei Y, Wu Q, Zhao P, Niu S, Huang J, Tan M (2020) Collaborative unsupervised domain adaptation for medical image diagnosis. IEEE Trans Image Process 29:7834\u20137844. https:\/\/doi.org\/10.1109\/TIP.2020.3006377","journal-title":"IEEE Trans Image Process"},{"key":"1297_CR51","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1038\/s41586-020-2012-7","volume":"579","author":"P Zhou","year":"2020","unstructured":"Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, Si HR, Zhu Y, Li B, Huang CL et al (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579:270\u2013273","journal-title":"Nature"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01297-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01297-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01297-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T15:33:06Z","timestamp":1711812786000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01297-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1297"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01297-9","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"9 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}