{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:38:37Z","timestamp":1778258317883,"version":"3.51.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164361","type":"print"},{"value":"9783031164378","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16437-8_53","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"554-563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Local Graph Fusion of\u00a0Multi-view MR Images for\u00a0Knee Osteoarthritis Diagnosis"],"prefix":"10.1007","author":[{"given":"Zixu","family":"Zhuang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Si","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lichi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"2","key":"53_CR1","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1148\/radiol.13122056","volume":"271","author":"H Alizai","year":"2014","unstructured":"Alizai, H., et al.: Cartilage lesion score: comparison of a quantitative assessment score with established semiquantitative MR scoring systems. Radiology 271(2), 479\u2013487 (2014)","journal-title":"Radiology"},{"issue":"3","key":"53_CR2","doi-asserted-by":"publisher","first-page":"e200165","DOI":"10.1148\/ryai.2021200165","volume":"3","author":"B Astuto","year":"2021","unstructured":"Astuto, B., et al.: Automatic deep learning-assisted detection and grading of abnormalities in knee MRI studies. Radiol. Artif. Intell. 3(3), e200165 (2021)","journal-title":"Radiol. Artif. Intell."},{"key":"53_CR3","doi-asserted-by":"crossref","unstructured":"Azcona, D., McGuinness, K., Smeaton, A.F.: A comparative study of existing and new deep learning methods for detecting knee injuries using the MRNet dataset. In: 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), pp. 149\u2013155. IEEE (2020)","DOI":"10.1109\/IDSTA50958.2020.9264030"},{"key":"53_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/978-3-030-80432-9_6","volume-title":"Medical Image Understanding and Analysis","author":"N Belton","year":"2021","unstructured":"Belton, N., et al.: Optimising knee injury detection with spatial attention and validating localisation ability. In: Papie\u017c, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds.) MIUA 2021. LNCS, vol. 12722, pp. 71\u201386. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-80432-9_6"},{"issue":"11","key":"53_CR5","doi-asserted-by":"publisher","first-page":"e1002699","DOI":"10.1371\/journal.pmed.1002699","volume":"15","author":"N Bien","year":"2018","unstructured":"Bien, N., et al.: Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of mrnet. PLoS Med. 15(11), e1002699 (2018)","journal-title":"PLoS Med."},{"key":"53_CR6","first-page":"1","volume":"18","author":"F Caliv\u00e0","year":"2021","unstructured":"Caliv\u00e0, F., Namiri, N.K., Dubreuil, M., Pedoia, V., Ozhinsky, E., Majumdar, S.: Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging. Nat. Rev. Rheumatol. 18, 1\u201310 (2021)","journal-title":"Nat. Rev. Rheumatol."},{"key":"53_CR7","unstructured":"Chen, S., Ma, K., Zheng, Y.: Med3D: transfer learning for 3D medical image analysis. arXiv preprint arXiv:1904.00625 (2019)"},{"key":"53_CR8","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2020)"},{"issue":"3","key":"53_CR9","doi-asserted-by":"publisher","first-page":"552","DOI":"10.2214\/AJR.17.18228","volume":"209","author":"ER Garwood","year":"2017","unstructured":"Garwood, E.R., Recht, M.P., White, L.M.: Advanced imaging techniques in the knee: benefits and limitations of new rapid acquisition strategies for routine knee MRI. Am. J. Roentgenol. 209(3), 552\u2013560 (2017)","journal-title":"Am. J. Roentgenol."},{"key":"53_CR10","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"},{"key":"53_CR11","doi-asserted-by":"publisher","first-page":"102508","DOI":"10.1016\/j.media.2022.102508","volume":"80","author":"J Huo","year":"2022","unstructured":"Huo, J., et al.: Automatic grading assessments for knee MRI cartilage defects via self-ensembling semi-supervised learning with dual-consistency. Med. Image Anal. 80, 102508 (2022)","journal-title":"Med. Image Anal."},{"key":"53_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1007\/978-3-030-59354-4_19","volume-title":"Predictive Intelligence in Medicine","author":"J Huo","year":"2020","unstructured":"Huo, J., et al.: A self-ensembling framework for semi-supervised knee cartilage defects assessment with dual-consistency. In: Rekik, I., Adeli, E., Park, S.H., Vald\u00e9s Hern\u00e1ndez, M.C. (eds.) PRIME 2020. LNCS, vol. 12329, pp. 200\u2013209. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59354-4_19"},{"issue":"2","key":"53_CR13","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, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"issue":"1","key":"53_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1038\/s41584-018-0130-5","volume":"15","author":"A Jamshidi","year":"2019","unstructured":"Jamshidi, A., Pelletier, J.P., Martel-Pelletier, J.: Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nat. Rev. Rheumatol. 15(1), 49\u201360 (2019)","journal-title":"Nat. Rev. Rheumatol."},{"issue":"3","key":"53_CR15","doi-asserted-by":"publisher","first-page":"180091","DOI":"10.1148\/ryai.2019180091","volume":"1","author":"F Liu","year":"2019","unstructured":"Liu, F., et al.: Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning. Radiol. Artif. Intell. 1(3), 180091 (2019)","journal-title":"Radiol. Artif. Intell."},{"issue":"1","key":"53_CR16","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1148\/radiol.2018172986","volume":"289","author":"F Liu","year":"2018","unstructured":"Liu, F., et al.: Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. Radiology 289(1), 160\u2013169 (2018)","journal-title":"Radiology"},{"key":"53_CR17","doi-asserted-by":"crossref","unstructured":"Nikolas, W., Jan, L., Carina, M., von Eisenhart-Rothe, R., Rainer, B.: Maintaining the spatial relation to improve deep-learning-assisted diagnosis for magnetic resonace imaging of the knee. Zeitschrift f\u00fcr Orthop\u00e4die und Unfallchirurgie 158(S 01), DKOU20-670 (2020)","DOI":"10.1055\/s-0040-1717495"},{"key":"53_CR18","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.joca.2006.02.029","volume":"14","author":"C Peterfy","year":"2006","unstructured":"Peterfy, C., Gold, G., Eckstein, F., Cicuttini, F., Dardzinski, B., Stevens, R.: MRI protocols for whole-organ assessment of the knee in osteoarthritis. Osteoarthr. Cartil. 14, 95\u2013111 (2006)","journal-title":"Osteoarthr. Cartil."},{"issue":"1","key":"53_CR19","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Networks"},{"key":"53_CR20","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.3389\/fmed.2020.600049","volume":"7","author":"L Si","year":"2021","unstructured":"Si, L., et al.: Knee cartilage thickness differs alongside ages: a 3-T magnetic resonance research upon 2,481 subjects via deep learning. Front. Med. 7, 1157 (2021)","journal-title":"Front. Med."},{"issue":"14","key":"53_CR21","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.1016\/j.jbiomech.2012.06.034","volume":"45","author":"T Suzuki","year":"2012","unstructured":"Suzuki, T., Hosseini, A., Li, J.S., Gill, T.J., IV., Li, G.: In vivo patellar tracking and patellofemoral cartilage contacts during dynamic stair ascending. J. Biomech. 45(14), 2432\u20132437 (2012)","journal-title":"J. Biomech."},{"key":"53_CR22","unstructured":"Tsai, C.H., Kiryati, N., Konen, E., Eshed, I., Mayer, A.: Knee injury detection using MRI with efficiently-layered network (ELNet). In: Medical Imaging with Deep Learning, pp. 784\u2013794. PMLR (2020)"},{"key":"53_CR23","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (ICLR), pp. 1\u201312 (2017)"},{"key":"53_CR24","unstructured":"Zhuang, Z., et al.: Knee cartilage defect assessment by graph representation and surface convolution. arXiv preprint arXiv:2201.04318 (2022)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16437-8_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:09:56Z","timestamp":1710252596000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_53","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}