{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T15:11:58Z","timestamp":1761664318256,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872397"},{"type":"electronic","value":"9783030872403"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87240-3_68","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T07:44:03Z","timestamp":1632383043000},"page":"709-718","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference"],"prefix":"10.1007","author":[{"given":"Mahsa","family":"Ghorbani","sequence":"first","affiliation":[]},{"given":"Mojtaba","family":"Bahrami","sequence":"additional","affiliation":[]},{"given":"Anees","family":"Kazi","sequence":"additional","affiliation":[]},{"given":"Mahdieh","family":"Soleymani Baghshah","sequence":"additional","affiliation":[]},{"given":"Hamid R.","family":"Rabiee","sequence":"additional","affiliation":[]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"68_CR1","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.neuroimage.2016.10.045","volume":"147","author":"A Abraham","year":"2017","unstructured":"Abraham, A., Milham, M.P., Di Martino, A., Craddock, R.C., Samaras, D., Thirion, B., Varoquaux, G.: Deriving reproducible biomarkers from multi-site resting-state data: An autism-based example. Neuroimage 147, 736\u2013745 (2017)","journal-title":"Neuroimage"},{"key":"68_CR2","doi-asserted-by":"crossref","unstructured":"Abrol, A., Fu, Z., Du, Y., Calhoun, V.D.: Multimodal data fusion of deep learning and dynamic functional connectivity features to predict alzheimer\u2019s disease progression. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4409\u20134413. IEEE (2019)","DOI":"10.1109\/EMBC.2019.8856500"},{"key":"68_CR3","doi-asserted-by":"crossref","unstructured":"Bi, X.a., Cai, R., Wang, Y., Liu, Y.: Effective diagnosis of alzheimer\u2019s disease via multimodal fusion analysis framework. Frontiers Genetics 10, 976 (2019)","DOI":"10.3389\/fgene.2019.00976"},{"key":"68_CR4","doi-asserted-by":"crossref","unstructured":"Bucilu\u01ce, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 535\u2013541 (2006)","DOI":"10.1145\/1150402.1150464"},{"key":"68_CR5","doi-asserted-by":"publisher","first-page":"133583","DOI":"10.1109\/ACCESS.2019.2941419","volume":"7","author":"Q Cai","year":"2019","unstructured":"Cai, Q., Wang, H., Li, Z., Liu, X.: A survey on multimodal data-driven smart healthcare systems: approaches and applications. IEEE Access 7, 133583\u2013133599 (2019)","journal-title":"IEEE Access"},{"key":"68_CR6","doi-asserted-by":"crossref","unstructured":"Craddock, C., et al.: The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Frontiers in Neuroinformatics 7 (2013)","DOI":"10.3389\/conf.fninf.2013.09.00041"},{"key":"68_CR7","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. arXiv preprint arXiv:1606.09375 (2016)"},{"issue":"6","key":"68_CR8","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2014","unstructured":"Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F.X., Alaerts, K., Anderson, J.S., Assaf, M., Bookheimer, S.Y., Dapretto, M., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"key":"68_CR9","unstructured":"Du, H., Feng, J., Feng, M.: Zoom in to where it matters: a hierarchical graph based model for mammogram analysis. arXiv preprint arXiv:1912.07517 (2019)"},{"key":"68_CR10","doi-asserted-by":"crossref","unstructured":"Ghorbani, M., Kazi, A., Baghshah, M.S., Rabiee, H.R., Navab, N.: Ra-gcn: Graph convolutional network for disease prediction problems with imbalanced data. arXiv preprint arXiv:2103.00221 (2021)","DOI":"10.1016\/j.media.2021.102272"},{"issue":"2","key":"68_CR11","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TRPMS.2018.2890359","volume":"3","author":"Z Guo","year":"2019","unstructured":"Guo, Z., Li, X., Huang, H., Guo, N., Li, Q.: Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans. Radiation Plasma Med. Sci. 3(2), 162\u2013169 (2019)","journal-title":"IEEE Trans. Radiation Plasma Med. Sci."},{"key":"68_CR12","unstructured":"Guyon, I.: Design of experiments of the nips 2003 variable selection benchmark. In: NIPS 2003Workshop on Feature Extraction and Feature Selection, vol. 253 (2003)"},{"key":"68_CR13","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"issue":"1","key":"68_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0211-0","volume":"3","author":"SC Huang","year":"2020","unstructured":"Huang, S.C., Pareek, A., Seyyedi, S., Banerjee, I., Lungren, M.P.: Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ digital Med. 3(1), 1\u20139 (2020)","journal-title":"NPJ digital Med."},{"key":"68_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-3-030-59728-3_55","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y Huang","year":"2020","unstructured":"Huang, Y., Chung, A.C.S.: Edge-variational graph convolutional networks for uncertainty-aware disease prediction. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 562\u2013572. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_55"},{"key":"68_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-3-030-20351-1_6","volume-title":"Information Processing in Medical Imaging","author":"A Kazi","year":"2019","unstructured":"Kazi, A., Shekarforoush, S., Arvind Krishna, S., Burwinkel, H., Vivar, G., Kort\u00fcm, K., Ahmadi, S.-A., Albarqouni, S., Navab, N.: InceptionGCN: receptive field aware graph convolutional network for disease prediction. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 73\u201385. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_6"},{"key":"68_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"68_CR18","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"issue":"1","key":"68_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"G Lee","year":"2019","unstructured":"Lee, G., Nho, K., Kang, B., Sohn, K.A., Kim, D.: Predicting alzheimer\u2019s disease progression using multi-modal deep learning approach. Sci. Rep. 9(1), 1\u201312 (2019)","journal-title":"Sci. Rep."},{"key":"68_CR20","doi-asserted-by":"crossref","unstructured":"Li, X., Duncan, J.: Braingnn: Interpretable brain graph neural network for fmri analysis. bioRxiv (2020)","DOI":"10.1101\/2020.05.16.100057"},{"issue":"6","key":"68_CR21","first-page":"1","volume":"21","author":"J Liu","year":"2020","unstructured":"Liu, J., Tan, G., Lan, W., Wang, J.: Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks. BMC Bioinformatics 21(6), 1\u201312 (2020)","journal-title":"BMC Bioinformatics"},{"key":"68_CR22","unstructured":"Marinescu, R.V., et al.: Tadpole challenge: Prediction of longitudinal evolution in alzheimer\u2019s disease. arXiv preprint arXiv:1805.03909 (2018)"},{"key":"68_CR23","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","volume":"48","author":"S Parisot","year":"2018","unstructured":"Parisot, S., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer\u2019s disease. Med. Image Anal. 48, 117\u2013130 (2018)","journal-title":"Med. Image Anal."},{"key":"68_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-319-66179-7_21","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017","author":"S Parisot","year":"2017","unstructured":"Parisot, S., Ktena, S.I., Ferrante, E., Lee, M., Moreno, R.G., Glocker, B., Rueckert, D.: Spectral Graph Convolutions for Population-Based Disease Prediction. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 177\u2013185. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_21"},{"key":"68_CR25","unstructured":"Pedregosa, F., et al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res.12, 2825\u20132830 (2011)"},{"issue":"1","key":"68_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-74399-w","volume":"11","author":"J Venugopalan","year":"2021","unstructured":"Venugopalan, J., Tong, L., Hassanzadeh, H.R., Wang, M.D.: Multimodal deep learning models for early detection of alzheimer\u2019s disease stage. Sci. Rep. 11(1), 1\u201313 (2021)","journal-title":"Sci. Rep."},{"key":"68_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/978-3-319-46723-8_14","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"T Xu","year":"2016","unstructured":"Xu, T., Zhang, H., Huang, X., Zhang, S., Metaxas, D.N.: Multimodal deep learning for cervical dysplasia diagnosis. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 115\u2013123. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_14"},{"key":"68_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/978-3-030-32248-9_89","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Yang","year":"2019","unstructured":"Yang, H., et al.: Interpretable multimodality embedding of cerebral cortex using attention graph network for identifying bipolar disorder. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 799\u2013807. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_89"},{"issue":"1","key":"68_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","volume":"6","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Tong, H., Xu, J., Maciejewski, R.: Graph convolutional networks: a comprehensive review. Comput. Soc. Networks 6(1), 1\u201323 (2019)","journal-title":"Comput. Soc. Networks"},{"key":"68_CR30","unstructured":"Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation (2002)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87240-3_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:42:09Z","timestamp":1673311329000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87240-3_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872397","9783030872403"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87240-3_68","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","order":11,"name":"conference_url","label":"Conference URL","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 CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}