{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T03:42:15Z","timestamp":1784691735520,"version":"3.55.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439926","type":"print"},{"value":"9783031439933","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43993-3_22","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"226-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Self-pruning Graph Neural Network for\u00a0Predicting Inflammatory Disease Activity in\u00a0Multiple Sclerosis from\u00a0Brain MR Images"],"prefix":"10.1007","author":[{"given":"Chinmay","family":"Prabhakar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei Bran","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes C.","family":"Paetzold","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timo","family":"Loehr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"M\u00fchlau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benedikt","family":"Wiestler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bjoern","family":"Menze","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"22_CR1","unstructured":"Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? arXiv preprint arXiv:2105.14491 (2021)"},{"key":"22_CR2","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"22_CR3","unstructured":"Durso-Finley, J., Falet, J.P., Nichyporuk, B., Douglas, A., Arbel, T.: Personalized prediction of future lesion activity and treatment effect in multiple sclerosis from baseline MRI. In: International Conference on Medical Imaging with Deep Learning, pp. 387\u2013406. PMLR (2022)"},{"issue":"1","key":"22_CR4","doi-asserted-by":"publisher","first-page":"5645","DOI":"10.1038\/s41467-022-33269-x","volume":"13","author":"JPR Falet","year":"2022","unstructured":"Falet, J.P.R., et al.: Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning. Nat. Commun. 13(1), 5645 (2022)","journal-title":"Nat. Commun."},{"issue":"3","key":"22_CR5","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1002\/ana.25808","volume":"88","author":"M Filippi","year":"2020","unstructured":"Filippi, M., et al.: Identifying progression in multiple sclerosis: new perspectives. Ann. Neurol. 88(3), 438\u2013452 (2020)","journal-title":"Ann. Neurol."},{"key":"22_CR6","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"175628642311618","DOI":"10.1177\/17562864231161892","volume":"16","author":"A Hapfelmeier","year":"2023","unstructured":"Hapfelmeier, A., et al.: Retrospective cohort study to devise a treatment decision score predicting adverse 24-month radiological activity in early multiple sclerosis. Ther. Adv. Neurol. Disord. 16, 17562864231161892 (2023)","journal-title":"Ther. Adv. Neurol. Disord."},{"issue":"12","key":"22_CR8","doi-asserted-by":"publisher","first-page":"1380","DOI":"10.1016\/j.amjmed.2020.05.049","volume":"133","author":"SL Hauser","year":"2020","unstructured":"Hauser, S.L., Cree, B.A.: Treatment of multiple sclerosis: a review. Am. J. Med. 133(12), 1380\u20131390 (2020)","journal-title":"Am. J. Med."},{"issue":"2","key":"22_CR9","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"},{"key":"22_CR10","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"22_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-030-87196-3_4","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"H Li","year":"2021","unstructured":"Li, H., et al.: Imbalance-aware self-supervised learning for 3D radiomic representations. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 36\u201346. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_4"},{"issue":"8","key":"22_CR12","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.3390\/life12081213","volume":"12","author":"CM Liu","year":"2022","unstructured":"Liu, C.M., Ta, V.D., Le, N.Q.K., Tadesse, D.A., Shi, C.: Deep neural network framework based on word embedding for protein glutarylation sites prediction. Life 12(8), 1213 (2022)","journal-title":"Life"},{"key":"22_CR13","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"22_CR14","unstructured":"Prabhakar, C., Li, H.B., Yang, J., Shit, S., Wiestler, B., Menze, B.: ViT-AE++: improving vision transformer autoencoder for self-supervised medical image representations. arXiv preprint arXiv:2301.07382 (2023)"},{"key":"22_CR15","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652\u2013660 (2017)"},{"key":"22_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"22_CR17","unstructured":"Schell, M., et al.: Automated brain extraction of multi-sequence MRI using artificial neural networks. European Congress of Radiology-ECR 2019 (2019)"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., Sun, Y.: Masked label prediction: unified message passing model for semi-supervised classification. arXiv preprint arXiv:2009.03509 (2020)","DOI":"10.24963\/ijcai.2021\/214"},{"issue":"7","key":"22_CR19","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/S1474-4422(13)70103-0","volume":"12","author":"MP Sormani","year":"2013","unstructured":"Sormani, M.P., Bruzzi, P.: MRI lesions as a surrogate for relapses in multiple sclerosis: a meta-analysis of randomised trials. Lancet Neurol. 12(7), 669\u2013676 (2013)","journal-title":"Lancet Neurol."},{"issue":"9","key":"22_CR20","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1038\/nrneurol.2013.146","volume":"9","author":"MP Sormani","year":"2013","unstructured":"Sormani, M.P., De Stefano, N.: Defining and scoring response to IFN-$$\\beta $$ in multiple sclerosis. Nat. Rev. Neurol. 9(9), 504\u2013512 (2013)","journal-title":"Nat. Rev. Neurol."},{"key":"22_CR21","unstructured":"Tousignant, A., Lema\u00eetre, P., Precup, D., Arnold, D.L., Arbel, T.: Prediction of disease progression in multiple sclerosis patients using deep learning analysis of MRI data. In: International Conference on Medical Imaging with Deep Learning, pp. 483\u2013492. PMLR (2019)"},{"key":"22_CR22","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Mixed transformer U-Net for medical image segmentation. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2390\u20132394. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746172"},{"issue":"5","key":"22_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"8","key":"22_CR25","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/S1474-4422(21)00095-8","volume":"20","author":"MP Wattjes","year":"2021","unstructured":"Wattjes, M.P., et al.: 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol. 20(8), 653\u2013670 (2021)","journal-title":"Lancet Neurol."},{"key":"22_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-319-46976-8_10","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"Y Yoo","year":"2016","unstructured":"Yoo, Y., et al.: Deep learning of brain lesion patterns for predicting future disease activity in patients with early symptoms of multiple sclerosis. In: Carneiro, G., et al. (eds.) LABELS\/DLMIA -2016. LNCS, vol. 10008, pp. 86\u201394. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46976-8_10"},{"key":"22_CR27","unstructured":"Zhang, X., He, L., Chen, K., Luo, Y., Zhou, J., Wang, F.: Multi-view graph convolutional network and its applications on neuroimage analysis for Parkinson\u2019s disease. In: AMIA Annual Symposium Proceedings, vol. 2018, p. 1147. American Medical Informatics Association (2018)"},{"key":"22_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocs.2018.07.003","volume":"28","author":"YD Zhang","year":"2018","unstructured":"Zhang, Y.D., Pan, C., Sun, J., Tang, C.: Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. J. Comput. Sci. 28, 1\u201310 (2018)","journal-title":"J. Comput. Sci."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43993-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:08:15Z","timestamp":1712074095000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}