{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T21:56:26Z","timestamp":1775858186135,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031185755","type":"print"},{"value":"9783031185762","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\/978-3-031-18576-2_6","type":"book-chapter","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T14:04:21Z","timestamp":1665151461000},"page":"55-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Image Feature Mapping Model for\u00a0Continuous Longitudinal Data Completion and\u00a0Generation of\u00a0Synthetic Patient Trajectories"],"prefix":"10.1007","author":[{"given":"Cl\u00e9ment","family":"Chadebec","sequence":"first","affiliation":[]},{"given":"Evi M. C.","family":"Huijben","sequence":"additional","affiliation":[]},{"given":"Josien P. W.","family":"Pluim","sequence":"additional","affiliation":[]},{"given":"St\u00e9phanie","family":"Allassonni\u00e8re","sequence":"additional","affiliation":[]},{"given":"Maureen A. J. M.","family":"van Eijnatten","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,8]]},"reference":[{"key":"6_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/978-3-030-00320-3_14","volume-title":"PRedictive Intelligence in MEdicine","author":"M Aghili","year":"2018","unstructured":"Aghili, M., Tabarestani, S., Adjouadi, M., Adeli, E.: Predictive modeling of longitudinal data for Alzheimer\u2019s disease diagnosis using RNNs. In: Rekik, I., Unal, G., Adeli, E., Park, S.H. (eds.) PRIME 2018. LNCS, vol. 11121, pp. 112\u2013119. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00320-3_14"},{"key":"6_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-319-67564-0_5","volume-title":"Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment","author":"L Bi","year":"2017","unstructured":"Bi, L., Kim, J., Kumar, A., Feng, D., Fulham, M.: Synthesis of positron emission tomography (PET) images via multi-channel generative adversarial networks (GANs). In: Cardoso, M.J., et al. (eds.) CMMI\/SWITCH\/RAMBO 2017. LNCS, vol. 10555, pp. 43\u201351. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67564-0_5"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Blackledge, M.D., et al.: Assessment of treatment response by total tumor volume and global apparent diffusion coefficient using diffusion-weighted MRI in patients with metastatic bone disease: a feasibility study. PLoS ONE 9(4), e91779 (2014)","DOI":"10.1371\/journal.pone.0091779"},{"key":"6_CR4","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. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9271\u20139280 (2018)","DOI":"10.1109\/CVPR.2018.00966"},{"key":"6_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/978-3-319-68612-7_71","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2017","author":"F Calimeri","year":"2017","unstructured":"Calimeri, F., Marzullo, A., Stamile, C., Terracina, G.: Biomedical data augmentation using generative adversarial neural networks. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10614, pp. 626\u2013634. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68612-7_71"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Chadebec, C., Thibeau-Sutre, E., Burgos, N., Allassonni\u00e8re, S.: Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3185773"},{"key":"6_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/978-3-030-87196-3_22","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"R Couronn\u00e9","year":"2021","unstructured":"Couronn\u00e9, R., Vernhet, P., Durrleman, S.: Longitudinal self-supervision to\u00a0disentangle inter-patient variability from\u00a0disease progression. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 231\u2013241. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_22"},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321\u2013331 (2018)","journal-title":"Neurocomputing"},{"key":"6_CR9","unstructured":"Ghosh, P., Sajjadi, M.S., Vergari, A., Black, M., Sch\u00f6lkopf, B.: From variational to deterministic autoencoders. In: International Conference on Learning Representations (ICLR) (2020)"},{"key":"6_CR10","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"6_CR11","unstructured":"Hussain, Z., Gimenez, F., Yi, D., Rubin, D.: Differential data augmentation techniques for medical imaging classification tasks. In: AMIA Annual Symposium Proceedings, vol. 2017, p. 979. American Medical Informatics Association (2017)"},{"key":"6_CR12","series-title":"NATO ASI Series","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-94-011-5014-9_5","volume-title":"Machine Learning","author":"MI Jordan","year":"1998","unstructured":"Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. In: Jordan, M.I. (ed.) Machine Learning. NATO ASI Series, pp. 105\u2013161. Springer, Dordrecht (1998). https:\/\/doi.org\/10.1007\/978-94-011-5014-9_5"},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"143212","DOI":"10.1109\/ACCESS.2021.3121609","volume":"9","author":"ST Kim","year":"2021","unstructured":"Kim, S.T., K\u00fc\u00e7\u00fckaslan, U., Navab, N.: Longitudinal brain MR image modeling using personalized memory for Alzheimer\u2019s disease. IEEE Access 9, 143212\u2013143221 (2021)","journal-title":"IEEE Access"},{"key":"6_CR14","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"issue":"3","key":"6_CR15","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.3390\/su13031224","volume":"13","author":"X Liu","year":"2021","unstructured":"Liu, X., Song, L., Liu, S., Zhang, Y.: A review of deep-learning-based medical image segmentation methods. Sustainability 13(3), 1224 (2021)","journal-title":"Sustainability"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"6_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1007\/978-3-030-20351-1_42","volume-title":"Information Processing in Medical Imaging","author":"M Louis","year":"2019","unstructured":"Louis, M., Couronn\u00e9, R., Koval, I., Charlier, B., Durrleman, S.: Riemannian geometry learning for disease progression modelling. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 542\u2013553. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_42"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Madani, A., Moradi, M., Karargyris, A., Syeda-Mahmood, T.: Chest X-ray generation and data augmentation for cardiovascular abnormality classification. In: Medical Imaging 2018: Image Processing, vol. 10574, pp. 415\u2013420. International Society for Optics and Photonics, SPIE (2018)","DOI":"10.1117\/12.2293971"},{"issue":"9","key":"6_CR19","doi-asserted-by":"publisher","first-page":"2443","DOI":"10.1093\/brain\/awn146","volume":"131","author":"SM Nestor","year":"2008","unstructured":"Nestor, S.M., et al.: Ventricular enlargement as a possible measure of Alzheimer\u2019s disease progression validated using the Alzheimer\u2019s disease neuroimaging initiative database. Brain 131(9), 2443\u20132454 (2008)","journal-title":"Brain"},{"key":"6_CR20","unstructured":"Ramchandran, S., Tikhonov, G., Kujanp\u00e4\u00e4, K., Koskinen, M., L\u00e4hdesm\u00e4ki, H.: Longitudinal variational autoencoder. In: Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 130, pp. 3898\u20133906. PMLR (2021)"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Salehinejad, H., Valaee, S., Dowdell, T., Colak, E., Barfett, J.: Generalization of deep neural networks for chest pathology classification in X-rays using generative adversarial networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 990\u2013994 (2018)","DOI":"10.1109\/ICASSP.2018.8461430"},{"issue":"1","key":"6_CR22","doi-asserted-by":"publisher","first-page":"16884","DOI":"10.1038\/s41598-019-52737-x","volume":"9","author":"V Sandfort","year":"2019","unstructured":"Sandfort, V., Yan, K., Pickhardt, P.J., Summers, R.M.: Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci. Rep. 9(1), 16884 (2019)","journal-title":"Sci. Rep."},{"issue":"5","key":"6_CR23","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285\u20131298 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-00536-8_1","volume-title":"Simulation and Synthesis in Medical Imaging","author":"H-C Shin","year":"2018","unstructured":"Shin, H.-C., et al.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, A., Goksel, O., Oguz, I., Burgos, N. (eds.) SASHIMI 2018. LNCS, vol. 11037, pp. 1\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00536-8_1"},{"issue":"1","key":"6_CR25","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019)","journal-title":"J. Big Data"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Wen, J., et al.: Convolutional neural networks for classification of Alzheimer\u2019s disease: overview and reproducible evaluation. Med. Image Anal. 63, 101694 (2020)","DOI":"10.1016\/j.media.2020.101694"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Liu, Z., Adeli, E., Pohl, K.M.: Longitudinal self-supervised learning. Med. Image Anal. 71, 102051 (2021)","DOI":"10.1016\/j.media.2021.102051"}],"container-title":["Lecture Notes in Computer Science","Deep Generative Models"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18576-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T14:06:14Z","timestamp":1665151574000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18576-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031185755","9783031185762"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18576-2_6","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":"8 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DGM4MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Deep Generative Models","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":"22 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":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dgm4miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dgm4miccai.github.io\/","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":"15","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":"12","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":"80% - 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":"3","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)"}}]}}