{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T20:29:55Z","timestamp":1764275395984,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030875886"},{"type":"electronic","value":"9783030875893"}],"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\/978-3-030-87589-3_22","type":"book-chapter","created":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T07:02:35Z","timestamp":1632553355000},"page":"209-218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes"],"prefix":"10.1007","author":[{"given":"Ignacio","family":"Sarasua","sequence":"first","affiliation":[]},{"given":"Sebastian","family":"P\u00f6lsterl","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Wachinger","sequence":"additional","affiliation":[]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"101952","DOI":"10.1016\/j.media.2020.101952","volume":"69","author":"C Baur","year":"2021","unstructured":"Baur, C., Denner, S., Wiestler, B., Navab, N., Albarqouni, S.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med. Image Anal. 69, 101952 (2021)","journal-title":"Med. Image Anal."},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1016\/j.neuroimage.2016.04.001","volume":"134","author":"M Bilgel","year":"2016","unstructured":"Bilgel, M., Prince, J.L., Wong, D.F., Resnick, S.M., Jedynak, B.M.: A multivariate nonlinear mixed effects model for longitudinal image analysis: application to amyloid imaging. Neuroimage 134, 658\u2013670 (2016)","journal-title":"Neuroimage"},{"issue":"8","key":"22_CR3","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301\u20131309 (2019)","journal-title":"Nat. Med."},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Cong, S., et al.: Building a surface atlas of hippocampal subfields from MRI scans using Freesurfer, FIRST and SPHARM. In: 2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 813\u2013816. IEEE (2014)","DOI":"10.1109\/MWSCAS.2014.6908539"},{"key":"22_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","volume":"73","author":"R Cui","year":"2019","unstructured":"Cui, R., Liu, M., Initiative, A.D.N., et al.: RNN-based longitudinal analysis for diagnosis of Alzheimer\u2019s disease. Comput. Med. Imaging Graph. 73, 1\u201310 (2019)","journal-title":"Comput. Med. Imaging Graph."},{"key":"22_CR6","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)"},{"key":"22_CR7","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 (2020)"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Feng, Y., Feng, Y., You, H., Zhao, X., Gao, Y.: MeshNet: mesh neural network for 3D shape representation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8279\u20138286 (2019)","DOI":"10.1609\/aaai.v33i01.33018279"},{"key":"22_CR9","doi-asserted-by":"publisher","unstructured":"Gao, R., et al.: Distanced LSTM: time-distanced gates in long short-term memory models for lung cancer detection. In: Suk, H.I., Liu, M., Yan, P., Lian, C. (eds.) Machine Learning in Medical Imaging, vol. 11861, pp. 310\u2013318. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32692-0_36","DOI":"10.1007\/978-3-030-32692-0_36"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 209\u2013216 (1997)","DOI":"10.1145\/258734.258849"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Gong, S., Chen, L., Bronstein, M., Zafeiriou, S.: Spiralnet++: a fast and highly efficient mesh convolution operator. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00509"},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"101852","DOI":"10.1016\/j.media.2020.101852","volume":"67","author":"B Guti\u00e9rrez-Becker","year":"2021","unstructured":"Guti\u00e9rrez-Becker, B., Sarasua, I., Wachinger, C.: Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks. Med. Image Anal. 67, 101852 (2021)","journal-title":"Med. Image Anal."},{"issue":"4","key":"22_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3322959","volume":"38","author":"R Hanocka","year":"2019","unstructured":"Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D.: MeshCNN: a network with an edge. ACM Trans. Graph. (TOG) 38(4), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"22_CR14","unstructured":"Hwang, S.J., Mehta, R.R., Kim, H.J., Johnson, S.C., Singh, V.: Sampling-free uncertainty estimation in gated recurrent units with applications to normative modeling in neuroimaging. In: Proceedings of the 35th Uncertainty in Artificial Intelligence Conference, vol. 115, pp. 809\u2013819 (2020)"},{"issue":"4","key":"22_CR15","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack","year":"2008","unstructured":"Jack, C.R., et al.: The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685\u2013691 (2008)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"6","key":"22_CR16","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1016\/j.neuron.2013.12.003","volume":"80","author":"CR Jack","year":"2013","unstructured":"Jack, C.R., Holtzman, D.M.: Biomarker modeling of Alzheimer\u2019s disease. Neuron 80(6), 1347\u20131358 (2013)","journal-title":"Neuron"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Lim, I., Dielen, A., Campen, M., Kobbelt, L.: A simple approach to intrinsic correspondence learning on unstructured 3D meshes. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)","DOI":"10.1007\/978-3-030-11015-4_26"},{"issue":"2","key":"22_CR19","doi-asserted-by":"publisher","first-page":"355","DOI":"10.3233\/JAD-2012-112210","volume":"30","author":"O Lindberg","year":"2012","unstructured":"Lindberg, O., et al.: Shape analysis of the hippocampus in Alzheimer\u2019s disease and subtypes of frontotemporal lobar degeneration. J. Alzheimer\u2019s Dis.: JAD 30(2), 355 (2012)","journal-title":"Journal of Alzheimer\u2019s disease: JAD"},{"key":"22_CR20","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neuroimage.2019.02.053","volume":"192","author":"RV Marinescu","year":"2019","unstructured":"Marinescu, R.V., et al.: Dive: a spatiotemporal progression model of brain pathology in neurodegenerative disorders. NeuroImage 192, 166\u2013177 (2019)","journal-title":"NeuroImage"},{"issue":"6","key":"22_CR21","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1080\/13543784.2017.1323868","volume":"26","author":"D Mehta","year":"2017","unstructured":"Mehta, D., Jackson, R., Paul, G., Shi, J., Sabbagh, M.: Why do trials for Alzheimer\u2019s disease drugs keep failing? A discontinued drug perspective for 2010\u20132015. Expert Opin. Investig. Drugs 26(6), 735\u2013739 (2017)","journal-title":"Expert opinion on investigational drugs"},{"issue":"3","key":"22_CR22","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/j.neuroimage.2011.02.046","volume":"56","author":"B Patenaude","year":"2011","unstructured":"Patenaude, B., Smith, S.M., Kennedy, D.N., Jenkinson, M.: A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage 56(3), 907\u2013922 (2011)","journal-title":"NeuroImage"},{"key":"22_CR23","doi-asserted-by":"publisher","unstructured":"Perek, S., Ness, L., Amit, M., Barkan, E., Amit, G.: Learning from longitudinal mammography studies. In: Shen, D. et al. (eds.) Medical Image Computing and Computer Assisted Intervention, vol. 11769, pp. 712\u2013720. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_79","DOI":"10.1007\/978-3-030-32226-7_79"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Ranjan, A., Bolkart, T., Sanyal, S., Black, M.J.: Generating 3D faces using convolutional mesh autoencoders. In: European Conference on Computer Vision (ECCV), pp. 704\u2013720 (2018)","DOI":"10.1007\/978-3-030-01219-9_43"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Ranjan, A., Bolkart, T., Sanyal, S., Black, M.J.: Generating 3D faces using convolutional mesh autoencoders. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 704\u2013720 (2018)","DOI":"10.1007\/978-3-030-01219-9_43"},{"key":"22_CR26","doi-asserted-by":"publisher","unstructured":"Santeramo, R., Withey, S., Montana, G.: Longitudinal detection of radiological abnormalities with time-modulated LSTM. In: Stoyanov D. et al. (eds.) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, vol. 11045, pp. 326\u2013333. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_37","DOI":"10.1007\/978-3-030-00889-5_37"},{"key":"22_CR27","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000\u20136010 (2017)"},{"issue":"11","key":"22_CR28","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.1158\/1078-0432.CCR-18-2495","volume":"25","author":"Y Xu","year":"2019","unstructured":"Xu, Y., et al.: Deep learning predicts lung cancer treatment response from serial medical imaging. Clin. Cancer Res. 25(11), 3266\u20133275 (2019)","journal-title":"Clinical Cancer Research"},{"key":"22_CR29","doi-asserted-by":"publisher","unstructured":"Yang, D., et al.: Deep image-to-image recurrent network with shape basis learning for automatic vertebra labeling in large-scale 3D CT volumes. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention, vol. 10435, pp. 498\u2013506. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_57","DOI":"10.1007\/978-3-319-66179-7_57"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87589-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,10]],"date-time":"2022-04-10T15:09:14Z","timestamp":1649603354000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87589-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030875886","9783030875893"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87589-3_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":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2021\/","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":"92","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":"71","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":"77% - 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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The workshop 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)"}}]}}