{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:34:11Z","timestamp":1715906051894},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61375063"],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s10489-024-05449-3","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T11:02:01Z","timestamp":1713870121000},"page":"5558-5575","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Auto-metric distribution propagation graph neural network with a meta-learning strategy for diagnosis of otosclerosis"],"prefix":"10.1007","volume":"54","author":[{"given":"Jiaoju","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jian","family":"Song","sequence":"additional","affiliation":[]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shuang","family":"Mao","sequence":"additional","affiliation":[]},{"given":"Mengli","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Yitao","family":"Mao","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6658-2187","authenticated-orcid":false,"given":"Muzhou","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Xuewen","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"issue":"2","key":"5449_CR1","doi-asserted-by":"publisher","first-page":"630","DOI":"10.3390\/jcm12020630","volume":"12","author":"M Bassiouni","year":"2023","unstructured":"Bassiouni M, Bauknecht H-C, Muench G, Olze H, Pohlan J (2023) Missed radiological diagnosis of otosclerosis in high-resolution computed tomography of the temporal bone-retrospective analysis of imaging, radiological reports, and request forms. J Clin Med 12(2):630","journal-title":"J Clin Med"},{"issue":"1","key":"5449_CR2","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.joto.2021.09.001","volume":"17","author":"M Hoste","year":"2022","unstructured":"Hoste M, Cabri-Wiltzer M, Hassid S, Degols J-C, Vilain J (2022) Hearing loss due to urate deposition in the middle ear: A case report and literature review. J Otol 17(1):50\u201353","journal-title":"J Otol"},{"issue":"7","key":"5449_CR3","doi-asserted-by":"publisher","first-page":"3327","DOI":"10.1007\/s00405-021-07036-5","volume":"279","author":"M Assiri","year":"2022","unstructured":"Assiri M, Khurayzi T, Alshalan A, Alsanosi A (2022) Cochlear implantation among patients with otosclerosis: a systematic review of clinical characteristics and outcomes. Eur Arch Otorhinolaryngol 279(7):3327\u20133339","journal-title":"Eur Arch Otorhinolaryngol"},{"issue":"12","key":"5449_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21037\/atm-21-1171","volume":"9","author":"W Tan","year":"2021","unstructured":"Tan W, Guan P, Wu L, Chen H, Li J, Ling Y, Fan T, Wang Y, Li J, Yan B (2021) The use of explainable artificial intelligence to explore types of fenestral otosclerosis misdiagnosed when using temporal bone high-resolution computed tomography. Ann Transl Med 9(12):1\u201320","journal-title":"Ann Transl Med"},{"issue":"7","key":"5449_CR5","doi-asserted-by":"publisher","first-page":"5206","DOI":"10.1007\/s00330-020-07568-0","volume":"31","author":"N Fujima","year":"2021","unstructured":"Fujima N, Andreu-Arasa VC, Onoue K, Weber PC, Hubbell RD, Setty BN, Sakai O (2021) Utility of deep learning for the diagnosis of otosclerosis on temporal bone ct. Eur Radiol 31(7):5206\u20135211","journal-title":"Eur Radiol"},{"key":"5449_CR6","doi-asserted-by":"crossref","unstructured":"K\u00f6sling S, Plontke SK, Bartel S (2020) Imaging of otosclerosis. In: R\u00f6Fo-Fortschritte Auf dem Gebiet der R\u00f6ntgenstrahlen und der Bildgebenden Verfahren, vol 192, pp745\u2013753 $$\\copyright $$ Georg Thieme Verlag KG","DOI":"10.1055\/a-1131-7980"},{"key":"5449_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107613","volume":"110","author":"Z Wang","year":"2021","unstructured":"Wang Z, Xiao Y, Li Y, Zhang J, Lu F, Hou M, Liu X (2021) Automatically discriminating and localizing covid-19 from community-acquired pneumonia on chest x-rays. Pattern Recognit 110:107613","journal-title":"Pattern Recognit"},{"key":"5449_CR8","doi-asserted-by":"crossref","unstructured":"Wang J, Luo Y, Wang Z, Hounye AH, Cao C, Hou M, Zhang J (2022) A cell phone app for facial acne severity assessment. Appl Intell, 1\u201320","DOI":"10.1007\/s10489-022-03774-z"},{"key":"5449_CR9","doi-asserted-by":"crossref","unstructured":"Wang Z, Hou M, Yan L, Dai Y, Yin Y, Liu X (2021) Deep learning for tracing esophageal motility function over time. Comput Methods Prog Biomed, 106212","DOI":"10.1016\/j.cmpb.2021.106212"},{"issue":"1","key":"5449_CR10","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/S2589-7500(20)30250-8","volume":"3","author":"A Bora","year":"2021","unstructured":"Bora A, Balasubramanian S, Babenko B, Virmani S, Venugopalan S, Mitani A, de Oliveira Marinho G, Cuadros J, Ruamviboonsuk P, Corrado GS et al (2021) Predicting the risk of developing diabetic retinopathy using deep learning. The Lancet Digital Health 3(1):10\u201319","journal-title":"The Lancet Digital Health"},{"issue":"1","key":"5449_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"X Yu","year":"2020","unstructured":"Yu X, Pang W, Xu Q, Liang M (2020) Mammographic image classification with deep fusion learning. Sci Rep 10(1):1\u201311","journal-title":"Sci Rep"},{"issue":"1","key":"5449_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-82289-y","volume":"11","author":"A Vaidyanathan","year":"2021","unstructured":"Vaidyanathan A, van der Lubbe MF, Leijenaar RT, van Hoof M, Zerka F, Miraglio B, Primakov S, Postma AA, Bruintjes TD, Bilderbeek MA et al (2021) Deep learning for the fully automated segmentation of the inner ear on mri. Sci Rep 11(1):1\u201314","journal-title":"Sci Rep"},{"issue":"1","key":"5449_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-90345-w","volume":"11","author":"X Zeng","year":"2021","unstructured":"Zeng X, Jiang Z, Luo W, Li H, Li H, Li G, Shi J, Wu K, Liu T, Lin X et al (2021) Efficient and accurate identification of ear diseases using an ensemble deep learning model. Sci Rep 11(1):1\u201310","journal-title":"Sci Rep"},{"key":"5449_CR14","doi-asserted-by":"crossref","unstructured":"Ma Y, Zhao S, Wang W, Li Y, King I (2022) Multimodality in meta-learning: A comprehensive survey. Knowledge-Based Systems, 108976","DOI":"10.1016\/j.knosys.2022.108976"},{"key":"5449_CR15","unstructured":"Qu M, Gao T, Xhonneux L-P, Tang J (2020) Few-shot relation extraction via bayesian meta-learning on relation graphs. In: International conference on machine learning, pp7867\u20137876 PMLR"},{"key":"5449_CR16","doi-asserted-by":"crossref","unstructured":"Cheng H, Zhou JT, Tay WP, Wen B (2023) Graph neural networks with triple attention for few-shot learning. IEEE Trans Multimed, 1\u201315","DOI":"10.1109\/TNNLS.2023.3293995"},{"key":"5449_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114795","volume":"176","author":"S Xu","year":"2021","unstructured":"Xu S, Xiang Y (2021) Frog-gnn: multi-perspective aggregation based graph neural network for few-shot text classification. Expert Syst Appl 176:114795","journal-title":"Expert Syst Appl"},{"issue":"1","key":"5449_CR18","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/TCSVT.2021.3058098","volume":"32","author":"C Chen","year":"2021","unstructured":"Chen C, Li K, Wei W, Zhou JT, Zeng Z (2021) Hierarchical graph neural networks for few-shot learning. IEEE Trans Circ Syst Video Technol 32(1):240\u2013252","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"5449_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3230043","volume":"60","author":"X Zuo","year":"2022","unstructured":"Zuo X, Yu X, Liu B, Zhang P, Tan X (2022) Fsl-egnn: Edge-labeling graph neural network for hyperspectral image few-shot classification. IEEE Trans Geosci Remote Sens 60:1\u201318","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5449_CR20","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.neunet.2022.08.003","volume":"155","author":"K Zhao","year":"2022","unstructured":"Zhao K, Zhang Z, Jiang B, Tang J (2022) Lglnn: Label guided graph learning-neural network for few-shot learning. Neural Netw 155:50\u201357","journal-title":"Neural Netw"},{"issue":"1","key":"5449_CR21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"P Saha","year":"2021","unstructured":"Saha P, Mukherjee D, Singh PK, Ahmadian A, Ferrara M, Sarkar R (2021) Graphcovidnet: A graph neural network based model for detecting covid-19 from ct scans and x-rays of chest. Sci Rep 11(1):1\u201316","journal-title":"Sci Rep"},{"issue":"8","key":"5449_CR22","doi-asserted-by":"publisher","first-page":"3141","DOI":"10.1109\/JBHI.2021.3053568","volume":"25","author":"X Song","year":"2021","unstructured":"Song X, Mao M, Qian X (2021) Auto-metric graph neural network based on a meta-learning strategy for the diagnosis of alzheimer\u2019s disease. IEEE J Biomed Health Inform 25(8):3141\u20133152","journal-title":"IEEE J Biomed Health Inform"},{"key":"5449_CR23","doi-asserted-by":"crossref","unstructured":"Yang L, Li L, Zhang Z, Zhou X, Zhou E, Liu Y (2020) Dpgn: Distribution propagation graph network for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp13390\u201313399","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"5449_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103803","volume":"77","author":"Y Zheng","year":"2022","unstructured":"Zheng Y, Zhao X, Yao L (2022) Copula-based transformer in eeg to assess visual discomfort induced by stereoscopic 3d. Biomed Signal Proces Control 77:103803","journal-title":"Biomed Signal Proces Control"},{"key":"5449_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106329","volume":"152","author":"S Dey","year":"2023","unstructured":"Dey S, Mitra S, Chakraborty S, Mondal D, Nasipuri M, Das N (2023) Gc-enc: A copula based ensemble of cnns for malignancy identification in breast histopathology and cytology images. Comput Biol Med 152:106329","journal-title":"Comput Biol Med"},{"key":"5449_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106211","volume":"207","author":"L Yan","year":"2021","unstructured":"Yan L, Liu D, Xiang Q, Luo Y, Wang T, Wu D, Chen H, Zhang Y, Li Q (2021) Psp net-based automatic segmentation network model for prostate magnetic resonance imaging. Comput Methods Programs Biomed 207:106211","journal-title":"Comput Methods Programs Biomed"},{"issue":"5","key":"5449_CR27","doi-asserted-by":"publisher","first-page":"5593","DOI":"10.1109\/TVT.2022.3152269","volume":"71","author":"FR Ghadi","year":"2022","unstructured":"Ghadi FR, Martin-Vega FJ, L\u00f3pez-Mart\u00ednez FJ (2022) Capacity of backscatter communication under arbitrary fading dependence. IEEE Trans Veh Technol 71(5):5593\u20135598","journal-title":"IEEE Trans Veh Technol"},{"issue":"6","key":"5449_CR28","doi-asserted-by":"publisher","first-page":"3210","DOI":"10.1111\/tgis.12823","volume":"25","author":"J Wang","year":"2021","unstructured":"Wang J, Wang Z, Deng M, Zou H, Wang K (2021) Heterogeneous spatiotemporal copula-based kriging for air pollution prediction. Trans GIS 25(6):3210\u20133232","journal-title":"Trans GIS"},{"key":"5449_CR29","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.neucom.2022.01.022","volume":"495","author":"A Salari","year":"2022","unstructured":"Salari A, Djavadifar A, Liu XR, Najjaran H (2022) Object recognition datasets and challenges: A review. Neurocomput 495:129\u2013152","journal-title":"Neurocomput"},{"key":"5449_CR30","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2021","unstructured":"Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 33:6999\u20137019","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5449_CR31","doi-asserted-by":"publisher","first-page":"24344","DOI":"10.1109\/ACCESS.2020.2971026","volume":"8","author":"S Zhai","year":"2020","unstructured":"Zhai S, Shang D, Wang S, Dong S (2020) Df-ssd: An improved ssd object detection algorithm based on densenet and feature fusion. IEEE access 8:24344\u201324357","journal-title":"IEEE access"},{"key":"5449_CR32","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.comcom.2022.05.035","volume":"192","author":"S Gaba","year":"2022","unstructured":"Gaba S, Budhiraja I, Kumar V, Garg S, Kaddoum G, Hassan MM (2022) A federated calibration scheme for convolutional neural networks: Models, applications and challenges. Comput Commun 192:144\u2013162","journal-title":"Comput Commun"},{"issue":"16","key":"5449_CR33","doi-asserted-by":"publisher","first-page":"13387","DOI":"10.1007\/s00521-022-07368-1","volume":"34","author":"P Cao","year":"2022","unstructured":"Cao P, Zhu Z, Wang Z, Zhu Y, Niu Q (2022) Applications of graph convolutional networks in computer vision. Neural Comput Appl 34(16):13387\u201313405","journal-title":"Neural Comput Appl"},{"key":"5449_CR34","doi-asserted-by":"crossref","unstructured":"Wells D, Knoll RM, Kozin E, Chen JX, Reinshagen KL, Staecker H, Curtin HD, McKenna MJ, Nadol Jr JB, Quesnel AM (2022) Otopathologic and computed tomography correlation of internal auditory canal diverticula in otosclerosis. Otol Neurotology 43(9):957\u2013962","DOI":"10.1097\/MAO.0000000000003665"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05449-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05449-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05449-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T16:07:03Z","timestamp":1715875623000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05449-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":34,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["5449"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05449-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"7 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2024","order":2,"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 conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}