{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T13:10:28Z","timestamp":1779282628202,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024MF102, ZR2023MF008, ZR2019MF073, ZR2024MH155"],"award-info":[{"award-number":["ZR2024MF102, ZR2023MF008, ZR2019MF073, ZR2024MH155"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372468, 82404100"],"award-info":[{"award-number":["62372468, 82404100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Major Basic Research Projects in Shandong Province","award":["ZR2023ZD32"],"award-info":[{"award-number":["ZR2023ZD32"]}]},{"name":"the Qingdao Natural Science Foundation","award":["23-2-1-161-zyyd-jch"],"award-info":[{"award-number":["23-2-1-161-zyyd-jch"]}]},{"name":"the Fundamental Research Funds for the Central Universities, China University of Petroleum","award":["20CX05001A"],"award-info":[{"award-number":["20CX05001A"]}]},{"name":"the Major Scientific and Technological Projects of CNPC","award":["ZD2019-183-008"],"award-info":[{"award-number":["ZD2019-183-008"]}]},{"name":"the Creative Research Team of Young Scholars at Universities in Shandong Province","award":["2019KJN019"],"award-info":[{"award-number":["2019KJN019"]}]},{"name":"the research and development of key technologies for clinical medicine of \u201cheart brain treatment\u201d, Yunnan Province","award":["No.202203AC100007"],"award-info":[{"award-number":["No.202203AC100007"]}]},{"name":"the State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development","award":["No.33550000-22-ZC0613-0243"],"award-info":[{"award-number":["No.33550000-22-ZC0613-0243"]}]},{"name":"the Jinan Clinical Medical Science and Technology Innovation Plan","award":["202225018"],"award-info":[{"award-number":["202225018"]}]},{"name":"the Key Research and Development Program of Yunnan provincial major science and technology special plan projects","award":["202403AA080002"],"award-info":[{"award-number":["202403AA080002"]}]},{"name":"Shandong Provincial Medical and Health Science and Technology Project","award":["202409010543"],"award-info":[{"award-number":["202409010543"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s00530-026-02254-5","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T13:28:33Z","timestamp":1773149313000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-difference-driven dual-stream contrast multi-view network for mammogram classification"],"prefix":"10.1007","volume":"32","author":[{"given":"Ruijia","family":"Tian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenteng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiongbin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baodi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"issue":"3","key":"2254_CR1","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R.L., Soerjomataram, I., Jemal, A.: Global cancer statistics 2022: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74(3), 229\u2013263 (2024)","journal-title":"CA Cancer J. Clin."},{"key":"2254_CR2","doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Miller, K.D., Wagle, N.S., Jemal, A.: Cancer statistics, 2023. CA: a cancer journal for clinicians 73, 1 (2023)","DOI":"10.3322\/caac.21763"},{"issue":"10","key":"2254_CR3","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1158\/1055-9965.EPI-15-0535","volume":"24","author":"CE DeSantis","year":"2015","unstructured":"DeSantis, C.E., Bray, F., Ferlay, J., Lortet-Tieulent, J., Anderson, B.O., Jemal, A.: International variation in female breast cancer incidence and mortality rates. Cancer Epidemiol. Biomark. Prevent. 24(10), 1495\u20131506 (2015)","journal-title":"Cancer Epidemiol. Biomark. Prevent."},{"key":"2254_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.breast.2022.08.010","volume":"66","author":"M Arnold","year":"2022","unstructured":"Arnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne, M., Vignat, J., Gralow, J.R., Cardoso, F., Siesling, S.: Current and future burden of breast cancer: global statistics for 2020 and 2040. Breast 66, 15\u201323 (2022)","journal-title":"Breast"},{"issue":"21","key":"2254_CR5","doi-asserted-by":"publisher","first-page":"1998","DOI":"10.1056\/NEJMoa1206809","volume":"367","author":"A Bleyer","year":"2012","unstructured":"Bleyer, A., Welch, H.G.: Effect of three decades of screening mammography on breast-cancer incidence. N. Engl. J. Med. 367(21), 1998\u20132005 (2012)","journal-title":"N. Engl. J. Med."},{"issue":"6","key":"2254_CR6","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.jacr.2024.02.019","volume":"21","author":"BL Niell","year":"2024","unstructured":"Niell, B.L., Jochelson, M.S., Amir, T., Brown, A., Adamson, M., Baron, P., Bennett, D.L., Chetlen, A., Dayaratna, S., Freer, P.E.: Acr appropriateness criteria\u00ae female breast cancer screening: 2023 update. J. Am. Coll. Radiol. 21(6), 126\u2013143 (2024)","journal-title":"J. Am. Coll. Radiol."},{"issue":"2","key":"2254_CR7","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1148\/radiology.217.2.r00nv07576","volume":"217","author":"F Sardanelli","year":"2000","unstructured":"Sardanelli, F., Zandrino, F., Imperiale, A., Bonaldo, E., Quartini, M.G., Cogorno, N.: Breast biphasic compression versus standard monophasic compression in x-ray mammography. Radiology 217(2), 576\u2013580 (2000)","journal-title":"Radiology"},{"issue":"2","key":"2254_CR8","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1148\/radiol.2021203633","volume":"299","author":"SG Veenhuizen","year":"2021","unstructured":"Veenhuizen, S.G., Lange, S.V., Bakker, M.F., Pijnappel, R.M., Mann, R.M., Monninkhof, E.M., Emaus, M.J., Koekkoek-Doll, P.K., Bisschops, R.H., Lobbes, M.B.: Supplemental breast mri for women with extremely dense breasts: results of the second screening round of the dense trial. Radiology 299(2), 278\u2013286 (2021)","journal-title":"Radiology"},{"key":"2254_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, H., Zhang, L., Cheng, L.: Mammographic mass detection based on convolution neural network. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3850\u20133855 (2018). IEEE","DOI":"10.1109\/ICPR.2018.8545557"},{"key":"2254_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, F., Luo, L., Sun, X., Zhou, Z., Li, X., Yu, Y., Wang, Y.: Cascaded generative and discriminative learning for microcalcification detection in breast mammograms. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12578\u201312586 (2019)","DOI":"10.1109\/CVPR.2019.01286"},{"key":"2254_CR11","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, D., Nailon, W.H., Davies, M.E., Laurenson, D.: A deep dual-path network for improved mammogram image processing. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1224\u20131228 (2019). IEEE","DOI":"10.1109\/ICASSP.2019.8682496"},{"key":"2254_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102204","volume":"73","author":"Z Yang","year":"2021","unstructured":"Yang, Z., Cao, Z., Zhang, Y., Tang, Y., Lin, X., Ouyang, R., Wu, M., Han, M., Xiao, J., Huang, L.: Momminet-v2: mammographic multi-view mass identification networks. Med. Image Anal. 73, 102204 (2021)","journal-title":"Med. Image Anal."},{"key":"2254_CR13","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.cmpb.2018.01.011","volume":"157","author":"H Chougrad","year":"2018","unstructured":"Chougrad, H., Zouaki, H., Alheyane, O.: Deep convolutional neural networks for breast cancer screening. Comput. Methods Progr. Biomed. 157, 19\u201330 (2018)","journal-title":"Comput. Methods Progr. Biomed."},{"issue":"3","key":"2254_CR14","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s11431-017-9317-3","volume":"62","author":"S Yu","year":"2019","unstructured":"Yu, S., Liu, L., Wang, Z., Dai, G., Xie, Y.: Transferring deep neural networks for the differentiation of mammographic breast lesions. Sci. China Technol. Sci. 62(3), 441\u2013447 (2019)","journal-title":"Sci. China Technol. Sci."},{"key":"2254_CR15","doi-asserted-by":"crossref","unstructured":"Zhu, W., Lou, Q., Vang, Y.S., Xie, X.: Deep multi-instance networks with sparse label assignment for whole mammogram classification. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 603\u2013611 (2017). Springer","DOI":"10.1007\/978-3-319-66179-7_69"},{"key":"2254_CR16","doi-asserted-by":"crossref","unstructured":"Lotter, W., Sorensen, G., Cox, D.: A multi-scale cnn and curriculum learning strategy for mammogram classification. In: International Workshop on Deep Learning in Medical Image Analysis, pp. 169\u2013177 (2017). Springer","DOI":"10.1007\/978-3-319-67558-9_20"},{"issue":"9","key":"2254_CR17","doi-asserted-by":"publisher","first-page":"3137","DOI":"10.1109\/TMI.2024.3389661","volume":"43","author":"B Han","year":"2024","unstructured":"Han, B., Sun, L., Li, C., Yu, Z., Jiang, W., Liu, W., Tao, D., Liu, B.: Deep location soft-embedding-based network with regional scoring for mammogram classification. IEEE Trans. Med. Imaging 43(9), 3137\u20133148 (2024)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2254_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103399","volume":"100","author":"L Sun","year":"2025","unstructured":"Sun, L., Han, B., Jiang, W., Liu, W., Liu, B., Tao, D., Yu, Z., Li, C.: Multi-scale region selection network in deep features for full-field mammogram classification. Med. Image Anal. 100, 103399 (2025)","journal-title":"Med. Image Anal."},{"key":"2254_CR19","doi-asserted-by":"crossref","unstructured":"Bekker, A.J., Greenspan, H., Goldberger, J.: A multi-view deep learning architecture for classification of breast microcalcifications. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 726\u2013730 (2016). Ieee","DOI":"10.1109\/ISBI.2016.7493369"},{"key":"2254_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2019.09.027","volume":"374","author":"Y Shachor","year":"2020","unstructured":"Shachor, Y., Greenspan, H., Goldberger, J.: A mixture of views network with applications to multi-view medical imaging. Neurocomputing 374, 1\u20139 (2020)","journal-title":"Neurocomputing"},{"issue":"3","key":"2254_CR21","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1109\/TCBB.2020.2970713","volume":"18","author":"C Li","year":"2020","unstructured":"Li, C., Xu, J., Liu, Q., Zhou, Y., Mou, L., Pu, Z., Xia, Y., Zheng, H., Wang, S.: Multi-view mammographic density classification by dilated and attention-guided residual learning. IEEE\/ACM Trans. Comput. Biol. Bioinf. 18(3), 1003\u20131013 (2020)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"2254_CR22","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, H., Wang, C., Tian, Y., Liu, F., Liu, Y., Elliott, M., McCarthy, D.J., Frazer, H., Carneiro, G.: Multi-view local co-occurrence and global consistency learning improve mammogram classification generalisation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 3\u201313 (2022). Springer","DOI":"10.1007\/978-3-031-16437-8_1"},{"key":"2254_CR23","doi-asserted-by":"crossref","unstructured":"Wang, X., Tan, T., Gao, Y., Han, L., Zhang, T., Lu, C., Beets-Tan, R., Su, R., Mann, R.: Disasymnet: Disentanglement of asymmetrical abnormality on bilateral mammograms using self-adversarial learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 57\u201367 (2023). Springer","DOI":"10.1007\/978-3-031-43990-2_6"},{"key":"2254_CR24","unstructured":"Wang, X., Gao, Y., Zhang, T., Han, L., Beets-Tan, R., Mann, R.: Looking for abnormalities using asymmetrical information from bilateral mammograms. In: Medical Imaging with Deep Learning (2022)"},{"issue":"4","key":"2254_CR25","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1109\/TMI.2019.2945514","volume":"39","author":"N Wu","year":"2019","unstructured":"Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrz\u0119bski, S., F\u00e9vry, T., Katsnelson, J., Kim, E.: Deep neural networks improve radiologists\u2019 performance in breast cancer screening. IEEE Trans. Med. Imaging 39(4), 1184\u20131194 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2254_CR26","doi-asserted-by":"crossref","unstructured":"Sun, Z., Jiang, H., Ma, L., Yu, Z., Xu, H.: Transformer based multi-view network for mammographic image classification. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 46\u201354 (2022). Springer","DOI":"10.1007\/978-3-031-16437-8_5"},{"key":"2254_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103073","volume":"70","author":"W Zhao","year":"2021","unstructured":"Zhao, W., Wang, R., Qi, Y., Lou, M., Wang, Y., Yang, Y., Deng, X., Ma, Y.: Bascnet: bilateral adaptive spatial and channel attention network for breast density classification in the mammogram. Biomed. Signal Process. Control 70, 103073 (2021)","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"2254_CR28","doi-asserted-by":"publisher","first-page":"848","DOI":"10.3390\/tomography10060065","volume":"10","author":"X Wen","year":"2024","unstructured":"Wen, X., Li, J., Yang, L.: Breast cancer diagnosis method based on cross-mammogram four-view interactive learning. Tomography 10(6), 848\u2013868 (2024)","journal-title":"Tomography"},{"key":"2254_CR29","doi-asserted-by":"crossref","unstructured":"Liu, X., Sun, L., Li, C., Han, B., Jiang, W., Yuan, T., Liu, W., Liu, Z., Yu, Z., Liu, B.: Lesion asymmetry screening assisted global awareness multi-view network for mammogram classification. IEEE Trans. Med. Imaging (2025)","DOI":"10.1109\/TMI.2025.3607877"},{"key":"2254_CR30","first-page":"1","volume":"61","author":"H Zhou","year":"2023","unstructured":"Zhou, H., Luo, F., Zhuang, H., Weng, Z., Gong, X., Lin, Z.: Attention multihop graph and multiscale convolutional fusion network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 61, 1\u201314 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"2254_CR31","doi-asserted-by":"crossref","unstructured":"Heath, M., Bowyer, K., Kopans, D., Kegelmeyer\u00a0Jr, P., Moore, R., Chang, K., Munishkumaran, S.: Current status of the digital database for screening mammography. In: Digital Mammography: Nijmegen, 1998, pp. 457\u2013460. Springer, Dordrecht (1998)","DOI":"10.1007\/978-94-011-5318-8_75"},{"key":"2254_CR32","unstructured":"Adam, K.D.B.J., et al.: A method for stochastic optimization. arXiv preprint arXiv:1412.69801412(6) (2014)"},{"key":"2254_CR33","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). Ieee","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2254_CR34","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198509844.001.0001","volume-title":"The statistical evaluation of medical tests for classification and prediction","author":"MS Pepe","year":"2003","unstructured":"Pepe, M.S.: The statistical evaluation of medical tests for classification and prediction. Oxford University Press, Oxford (2003)"},{"key":"2254_CR35","unstructured":"Powers, D.M.: Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061 (2020)"},{"issue":"3","key":"2254_CR36","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/TKDE.2005.50","volume":"17","author":"J Huang","year":"2005","unstructured":"Huang, J., Ling, C.X.: Using auc and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17(3), 299\u2013310 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"6943","key":"2254_CR37","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1136\/bmj.308.6943.1552","volume":"308","author":"DG Altman","year":"1994","unstructured":"Altman, D.G., Bland, J.M.: Diagnostic tests. 1: Sensitivity and specificity. BMJ: Br. Med. J. 308(6943), 1552 (1994)","journal-title":"BMJ: Br. Med. J."},{"key":"2254_CR38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"10","key":"2254_CR39","doi-asserted-by":"publisher","first-page":"2698","DOI":"10.1109\/TMI.2020.3042773","volume":"40","author":"X Ouyang","year":"2020","unstructured":"Ouyang, X., Karanam, S., Wu, Z., Chen, T., Huo, J., Zhou, X.S., Wang, Q., Cheng, J.-Z.: Learning hierarchical attention for weakly-supervised chest x-ray abnormality localization and diagnosis. IEEE Trans. Med. Imaging 40(10), 2698\u20132710 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2254_CR40","doi-asserted-by":"crossref","unstructured":"Van\u00a0Tulder, G., Tong, Y., Marchiori, E.: Multi-view analysis of unregistered medical images using cross-view transformers. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 104\u2013113 (2021). Springer","DOI":"10.1007\/978-3-030-87199-4_10"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-026-02254-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-026-02254-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-026-02254-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T12:32:48Z","timestamp":1779280368000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-026-02254-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,10]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["2254"],"URL":"https:\/\/doi.org\/10.1007\/s00530-026-02254-5","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,10]]},"assertion":[{"value":"1 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","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 there are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"179"}}