{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:51:07Z","timestamp":1743018667100,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031169182"},{"type":"electronic","value":"9783031169199"}],"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\/978-3-031-16919-9_6","type":"book-chapter","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T19:02:37Z","timestamp":1663700557000},"page":"60-70","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mixup Augmentation Improves Age Prediction from T1-Weighted Brain MRI Scans"],"prefix":"10.1007","author":[{"given":"Lara","family":"Dular","sequence":"first","affiliation":[]},{"given":"\u017diga","family":"\u0160piclin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","unstructured":"Bron, E.E., et al.: Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer\u2019s disease. NeuroImage: Clin. 31, 102712 (2021). https:\/\/doi.org\/10.1016\/j.nicl.2021.102712. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S221315822100156X","DOI":"10.1016\/j.nicl.2021.102712"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Cheng, J., et al.: Brain age estimation from mri using cascade networks with ranking loss. In: IEEE Transactions on Medical Imaging, pp. 1\u20131 (2021). https:\/\/doi.org\/10.1109\/TMI.2021.3085948","DOI":"10.1109\/TMI.2021.3085948"},{"key":"6_CR3","doi-asserted-by":"publisher","unstructured":"Cole, J.H., et al.: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 163, 115\u2013124 (2017). https:\/\/doi.org\/10.1016\/j.neuroimage.2017.07.059. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811917306407","DOI":"10.1016\/j.neuroimage.2017.07.059"},{"key":"6_CR4","unstructured":"Eaton-Rosen, Z., Bragman, F., Ourselin, S., Cardoso, M.J.: Improving data augmentation for medical image segmentation. In: International Conference on Medical Imaging with Deep Learning, p. 3 (2018)"},{"key":"6_CR5","doi-asserted-by":"publisher","unstructured":"Fonov, V., Evans, A., McKinstry, R., Almli, C., Collins, D.: Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47, S102 (2009). https:\/\/doi.org\/10.1016\/S1053-8119(09)70884-5. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811909708845","DOI":"10.1016\/S1053-8119(09)70884-5"},{"key":"6_CR6","unstructured":"Galdran, A., Carneiro, G., Ballester, M.A.G.: Balanced-MixUp for highly imbalanced medical image classification. arXiv:2109.09850 [cs] (2021). http:\/\/arxiv.org\/abs\/2109.09850, arXiv: 2109.09850"},{"key":"6_CR7","doi-asserted-by":"publisher","unstructured":"Hwang, S.H., Whang, S.E.: MixR: data mixing augmentation for regression (2021). https:\/\/doi.org\/10.48550\/ARXIV.2106.03374. https:\/\/arxiv.org\/abs\/2106.03374","DOI":"10.48550\/ARXIV.2106.03374"},{"key":"6_CR8","doi-asserted-by":"publisher","unstructured":"Isaksson, L.J., et al.: Mixup (sample pairing) can improve the performance of deep segmentation networks. J. Artif. Intell. Soft Comput. Res. 12(1), 29\u201339 (2022). https:\/\/doi.org\/10.2478\/jaiscr-2022-0003. https:\/\/www.sciendo.com\/article\/10.2478\/jaiscr-2022-0003","DOI":"10.2478\/jaiscr-2022-0003"},{"issue":"1","key":"6_CR9","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1002\/jmri.22003","volume":"31","author":"JV Manj\u00f3n","year":"2010","unstructured":"Manj\u00f3n, J.V., Coup\u00e9, P., Mart\u00ed-Bonmat\u00ed, L., Collins, D.L., Robles, M.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31(1), 192\u2013203 (2010). https:\/\/doi.org\/10.1002\/jmri.22003","journal-title":"J. Magn. Reson. Imaging"},{"issue":"12","key":"6_CR10","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","volume":"22","author":"DS Marcus","year":"2010","unstructured":"Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677\u20132684 (2010). https:\/\/doi.org\/10.1162\/jocn.2009.21407","journal-title":"J. Cogn. Neurosci."},{"key":"6_CR11","doi-asserted-by":"publisher","unstructured":"Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: Global image registration using a symmetric block-matching approach. J. Med. Imaging 1(2), 1\u20136 (2014). https:\/\/doi.org\/10.1117\/1.JMI.1.2.024003. https:\/\/doi.org\/10.1117\/1.JMI.1.2.024003","DOI":"10.1117\/1.JMI.1.2.024003"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Panfilov, E., Tiulpin, A., Klein, S., Nieminen, M.T., Saarakkala, S.: Improving robustness of deep learning based knee MRI segmentation: Mixup and adversarial domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00057"},{"key":"6_CR13","doi-asserted-by":"publisher","unstructured":"Shafto, M.A., et al.: The Cambridge centre for ageing and neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol. 14 (2014). https:\/\/doi.org\/10.1186\/s12883-014-0204-1. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4219118\/","DOI":"10.1186\/s12883-014-0204-1"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Taylor, J.R., et al.: The Cambridge centre for ageing and neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage 144(Pt B), 262\u2013269 (2017). https:\/\/doi.org\/10.1016\/j.neuroimage.2015.09.018","DOI":"10.1016\/j.neuroimage.2015.09.018"},{"key":"6_CR15","unstructured":"The Cambridge Centre for Ageing and Neuroscience (CamCAN). http:\/\/fcon_1000.projects.nitrc.org\/indi\/abide\/abide_I.html"},{"issue":"6","key":"6_CR16","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310\u20131320 (2010). https:\/\/doi.org\/10.1109\/TMI.2010.2046908","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR17","unstructured":"Verma, V., et al.: Manifold Mixup: better representations by interpolating hidden states. In: Proceedings of the 36th International Conference on Machine Learning, pp. 6438\u20136447. PMLR (2019). https:\/\/proceedings.mlr.press\/v97\/verma19a.html"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Xie, T., Cheng, X., Liu, M., Deng, J., Wang, X., Liu, M.: Thumbnail: a novel data augmentation for convolutional neural network. arXiv:2103.05342 [cs] (2021). http:\/\/arxiv.org\/abs\/2103.05342. arXiv: 2103.05342","DOI":"10.1145\/3474085.3475302"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. arXiv:1905.04899 [cs] (2019). http:\/\/arxiv.org\/abs\/1905.04899. arXiv: 1905.04899","DOI":"10.1109\/ICCV.2019.00612"},{"key":"6_CR20","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16919-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T19:03:37Z","timestamp":1663700617000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16919-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031169182","9783031169199"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16919-9_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on PRedictive Intelligence In MEdicine","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":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"prime2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/basira-lab.com\/prime-miccai-2022\/","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":"20","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":"19","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":"95% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}