{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:06:13Z","timestamp":1761253573425,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030871956"},{"type":"electronic","value":"9783030871963"}],"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\/978-3-030-87196-3_22","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"231-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Longitudinal Self-supervision to\u00a0Disentangle Inter-patient Variability from\u00a0Disease Progression"],"prefix":"10.1007","author":[{"given":"Rapha\u00ebl","family":"Couronn\u00e9","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Vernhet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stanley","family":"Durrleman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"1","key":"22_CR1","doi-asserted-by":"publisher","first-page":"18113","DOI":"10.1038\/s41598-019-54653-6","volume":"9","author":"SI Berchuck","year":"2019","unstructured":"Berchuck, S.I., Mukherjee, S., Medeiros, F.A.: Estimating rates of progression and predicting future visual fields in glaucoma using a deep variational autoencoder. Sci. Rep. 9(1), 18113 (2019)","journal-title":"Sci. Rep."},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Bigio, E., Hynan, L., Sontag, E., Satumtira, S., White, C.: Synapse loss is greater in presenile than senile onset Alzheimer disease: implications for the cognitive reserve hypothesis. Neuropathol. Appl. Neurobiol. 28(3), 218\u2013227 (2002)","DOI":"10.1046\/j.1365-2990.2002.00385.x"},{"key":"22_CR3","unstructured":"Blondel, M., Teboul, O., Berthet, Q., Djolonga, J.: Fast differentiable sorting and ranking. In: International Conference on Machine Learning, pp. 950\u2013959. PMLR (2020). ISSN 2640\u20133498"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"B\u00f4ne, A., Louis, M., Martin, B., Durrleman, S.: Deformetrica 4: an open-source software for statistical shape analysis. In: ShapeMI @ MICCAI 2018, Granada, Spain, November 2018 (2018). https:\/\/hal.inria.fr\/hal-01874752","DOI":"10.1007\/978-3-030-04747-4_1"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Bouchacourt, D., Tomioka, R., Nowozin, S.: Multi-level variational autoencoder: learning disentangled representations from grouped observations. CoRR abs\/1705.08841 (2017)","DOI":"10.1609\/aaai.v32i1.11867"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"B\u00f4ne, A., Colliot, O., Durrleman, S.: Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms, pp. 9271\u20139280 (2018)","DOI":"10.1109\/CVPR.2018.00966"},{"key":"22_CR7","unstructured":"Dalca, A.V., Rakic, M., Guttag, J., Sabuncu, M.R.: Learning conditional deformable templates with convolutional networks. arXiv:1908.02738 [cs, eess] (2019). arXiv: 1908.02738"},{"key":"22_CR8","doi-asserted-by":"publisher","unstructured":"Gao, L., Pan, H., Liu, F., Xie, X., Zhang, Z., Han, J.: Brain disease diagnosis using deep learning features from longitudinal MR images. In: Cai, Y., Ishikawa, Y., Xu, J. (eds.) Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data. LNCS, vol. 10987, pp. 327\u2013339. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-96890-2_27","DOI":"10.1007\/978-3-319-96890-2_27"},{"key":"22_CR9","unstructured":"Grathwohl, W., Wilson, A.: Disentangling space and time in video with hierarchical variational auto-encoders. arXiv preprint arXiv:1612.04440 (2016)"},{"key":"22_CR10","unstructured":"Higgins, I., et al.: beta-VAE: learning basic visual concepts with a constrained variational framework (2016)"},{"key":"22_CR11","unstructured":"Hsu, W.N., Zhang, Y., Glass, J.: Unsupervised learning of disentangled and interpretable representations from sequential data. Adv. Neural Inf. Process. Syst. 30, 1878\u20131889 (2017)"},{"key":"22_CR12","unstructured":"Kim, H., Mnih, A.: Disentangling by factorising. arXiv:1802.05983 [cs, stat] (2019). arXiv: 1802.05983"},{"key":"22_CR13","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"22_CR14","unstructured":"Krebs, J., Delingette, H., Ayache, N., Mansi, T.: Learning a generative motion model from image sequences based on a latent motion matrix. arXiv:2011.01741 [cs] (2020). arXiv: 2011.01741"},{"key":"22_CR15","unstructured":"Li, Y., Mandt, S.: Disentangled sequential autoencoder. arXiv preprint arXiv:1803.02991 (2018)"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Louis, M., Charlier, B., Durrleman, S.: Geodesic discriminant analysis for manifold-valued data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 332\u2013340 (2018)","DOI":"10.1109\/CVPRW.2018.00073"},{"key":"22_CR17","doi-asserted-by":"publisher","unstructured":"Louis, M., Couronn\u00e9, R., Koval, I., Charlier, B., Durrleman, S.: Riemannian geometry learning for disease progression modelling. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds.) Information Processing in Medical Imaging. IPMI 2019. LNCS, vol. 11492, pp. 542\u2013553. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_42","DOI":"10.1007\/978-3-030-20351-1_42"},{"key":"22_CR18","unstructured":"Mathieu, E., Rainforth, T., Siddharth, N., Teh, Y.W.: Disentangling disentanglement in variational autoencoders. In: International Conference on Machine Learning, pp. 4402\u20134412. PMLR (2019)"},{"key":"22_CR19","doi-asserted-by":"publisher","unstructured":"Ravi, D., Alexander, D.C., Oxtoby, N.P.: Degenerative adversarial neuroimage nets: Generating images that mimic disease progression. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019. MICCAI 2019. LNCS, vol. 11766, pp. 164\u2013172. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_19","DOI":"10.1007\/978-3-030-32248-9_19"},{"key":"22_CR20","unstructured":"Routier, A., et al.: Clinica: an open source software platform for reproducible clinical neuroscience studies (2019). https:\/\/hal.inria.fr\/hal-02308126"},{"key":"22_CR21","unstructured":"Schiratti, J.B., Allassonniere, S., Colliot, O., Durrleman, S.: Learning spatiotemporal trajectories from manifold-valued longitudinal data. In: Advances in Neural Information Processing Systems, pp. 2404\u20132412 (2015)"},{"key":"22_CR22","doi-asserted-by":"publisher","unstructured":"Xia, T., Chartsias, A., Tsaftaris, S.A.: Consistent brain ageing synthesis. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019. LNCS, vol. 11767, pp. 750\u2013758. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_82","DOI":"10.1007\/978-3-030-32251-9_82"},{"key":"22_CR23","unstructured":"Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: Guyon, I. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 3391\u20133401. Curran Associates, Inc. (2017)"},{"key":"22_CR24","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Song, Y., Qi, H.: Age progression\/regression by conditional adversarial autoencoder. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4352\u20134360. IEEE, Honolulu, HI (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.463","DOI":"10.1109\/CVPR.2017.463"},{"key":"22_CR25","unstructured":"Zhao, Q., Liu, Z., Adeli, E., Pohl, K.M.: LSSL: Longitudinal Self-Supervised Learning. arXiv:2006.06930 [cs, stat] (2020). http:\/\/arxiv.org\/abs\/2006.06930. arXiv: 2006.06930"}],"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\/978-3-030-87196-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:35:13Z","timestamp":1673310913000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87196-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871956","9783030871963"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87196-3_22","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)"}}]}}