{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:37:19Z","timestamp":1743136639736,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164514"},{"type":"electronic","value":"9783031164521"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_43","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"448-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accurate and\u00a0Explainable Image-Based Prediction Using a\u00a0Lightweight Generative Model"],"prefix":"10.1007","author":[{"given":"Chiara","family":"Mauri","sequence":"first","affiliation":[]},{"given":"Stefano","family":"Cerri","sequence":"additional","affiliation":[]},{"given":"Oula","family":"Puonti","sequence":"additional","affiliation":[]},{"given":"Mark","family":"M\u00fchlau","sequence":"additional","affiliation":[]},{"given":"Koen","family":"Van Leemput","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"43_CR1","unstructured":"https:\/\/sabuncu.engineering.cornell.edu\/software-projects\/relevance-voxel-machine-rvoxm-code-release\/"},{"key":"43_CR2","unstructured":"https:\/\/github.com\/QingyuZhao\/VAE-for-Regression"},{"key":"43_CR3","unstructured":"Adebayo, J., et al.: Sanity checks for saliency maps. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018)"},{"key":"43_CR4","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","volume":"166","author":"F Alfaro-Almagro","year":"2018","unstructured":"Alfaro-Almagro, F., et al.: Image processing and quality control for the first 10,000 brain imaging datasets from UK biobank. Neuroimage 166, 400\u2013424 (2018)","journal-title":"Neuroimage"},{"key":"43_CR5","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.neuroimage.2016.02.079","volume":"145","author":"MR Arbabshirani","year":"2017","unstructured":"Arbabshirani, M.R., et al.: Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 145, 137\u2013165 (2017)","journal-title":"Neuroimage"},{"key":"43_CR6","doi-asserted-by":"crossref","unstructured":"Arun, N., et al.: Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging. Radiol. Artif. Intell. 3(6), e200267 (2021)","DOI":"10.1148\/ryai.2021200267"},{"issue":"6","key":"43_CR7","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1006\/nimg.2000.0582","volume":"11","author":"J Ashburner","year":"2000","unstructured":"Ashburner, J., et al.: Voxel-based morphometry-the methods. Neuroimage 11(6), 805\u2013821 (2000)","journal-title":"Neuroimage"},{"key":"43_CR8","first-page":"1803","volume":"11","author":"D Baehrens","year":"2010","unstructured":"Baehrens, D., et al.: How to explain individual classification decisions. J. Mach. Learn. Res. 11, 1803\u20131831 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"43_CR9","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TMI.2011.2162961","volume":"31","author":"NK Batmanghelich","year":"2011","unstructured":"Batmanghelich, N.K., et al.: Generative-discriminative basis learning for medical imaging. IEEE Trans. Med. Imaging 31(1), 51\u201369 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"43_CR10","unstructured":"Bishop, C.M., Nasrabadi, N.M.: Pattern Recognition and Machine Learning, vol. 4, Chap. 12. Springer, New York (2006)"},{"issue":"3","key":"43_CR11","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1006\/nimg.2001.0862","volume":"14","author":"M Chung","year":"2001","unstructured":"Chung, M., et al.: A unified statistical approach to deformation-based morphometry. NeuroImage 14(3), 595\u2013606 (2001)","journal-title":"NeuroImage"},{"key":"43_CR12","series-title":"Healthy Ageing and Longevity","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/978-3-030-24970-0_19","volume-title":"Biomarkers of Human Aging","author":"JH Cole","year":"2019","unstructured":"Cole, J.H., Franke, K., Cherbuin, N.: Quantification of the biological age of the brain using neuroimaging. In: Moskalev, A. (ed.) Biomarkers of Human Aging. HAL, vol. 10, pp. 293\u2013328. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-24970-0_19"},{"issue":"6","key":"43_CR13","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1006\/nimg.2001.0937","volume":"14","author":"C Davatzikos","year":"2001","unstructured":"Davatzikos, C., et al.: Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy. NeuroImage 14(6), 1361\u20131369 (2001)","journal-title":"NeuroImage"},{"issue":"6","key":"43_CR14","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2014","unstructured":"Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"issue":"2","key":"43_CR15","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1023\/A:1007413511361","volume":"29","author":"P Domingos","year":"1997","unstructured":"Domingos, P., et al.: On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. 29(2), 103\u2013130 (1997)","journal-title":"Mach. Learn."},{"issue":"4","key":"43_CR16","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1017\/S1041610209009405","volume":"21","author":"KA Ellis","year":"2009","unstructured":"Ellis, K.A., et al.: The Australian imaging, biomarkers and lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer\u2019s disease. Int. Psychogeriatrics 21(4), 672\u2013687 (2009)","journal-title":"Int. Psychogeriatrics"},{"issue":"3","key":"43_CR17","first-page":"1","volume":"1341","author":"D Erhan","year":"2009","unstructured":"Erhan, D., et al.: Visualizing higher-layer features of a deep network. Univ. Montr. 1341(3), 1 (2009)","journal-title":"Univ. Montr."},{"issue":"20","key":"43_CR18","doi-asserted-by":"publisher","first-page":"11050","DOI":"10.1073\/pnas.200033797","volume":"97","author":"B Fischl","year":"2000","unstructured":"Fischl, B., et al.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. PNAS 97(20), 11050 (2000)","journal-title":"PNAS"},{"issue":"9","key":"43_CR19","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1093\/cercor\/bhn232","volume":"19","author":"AM Fjell","year":"2009","unstructured":"Fjell, A.M., et al.: High consistency of regional cortical thinning in aging across multiple samples. Cereb. Cortex 19(9), 2001\u20132012 (2009). https:\/\/doi.org\/10.1093\/cercor\/bhn232","journal-title":"Cereb. Cortex"},{"issue":"11","key":"43_CR20","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi, M., et al.: The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3(11), e745\u2013e750 (2021)","journal-title":"Lancet Digit. Health"},{"issue":"9","key":"43_CR21","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1038\/nn.4361","volume":"19","author":"MF Glasser","year":"2016","unstructured":"Glasser, M.F., et al.: The human connectome project\u2019s neuroimaging approach. Nat. Neurosci. 19(9), 1175\u20131187 (2016)","journal-title":"Nat. Neurosci."},{"key":"43_CR22","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neuroimage.2013.10.067","volume":"87","author":"S Haufe","year":"2014","unstructured":"Haufe, S., et al.: On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96\u2013110 (2014)","journal-title":"Neuroimage"},{"key":"43_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116276","volume":"206","author":"T He","year":"2020","unstructured":"He, T., et al.: Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. NeuroImage 206, 116276 (2020)","journal-title":"NeuroImage"},{"key":"43_CR24","unstructured":"Jack Jr., C.R., et al.: The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med. 27(4), 685\u2013691 (2008)"},{"issue":"10","key":"43_CR25","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1038\/s41593-019-0471-7","volume":"22","author":"T Kaufmann","year":"2019","unstructured":"Kaufmann, T., et al.: Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat. Neurosci. 22(10), 1617\u20131623 (2019)","journal-title":"Nat. Neurosci."},{"key":"43_CR26","unstructured":"Kingma, D.P., et al.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"43_CR27","unstructured":"Ng, A.Y., et al.: On discriminative vs. generative classifiers: a comparison of logistic regression and Naive Bayes. In: Advances In Neural Information Processing Systems, pp. 841\u2013848 (2002)"},{"key":"43_CR28","doi-asserted-by":"publisher","first-page":"101871","DOI":"10.1016\/j.media.2020.101871","volume":"68","author":"H Peng","year":"2021","unstructured":"Peng, H., et al.: Accurate brain age prediction with lightweight deep neural networks. Med. Image Anal. 68, 101871 (2021)","journal-title":"Med. Image Anal."},{"key":"43_CR29","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1613\/jair.1.13200","volume":"73","author":"G Ras","year":"2022","unstructured":"Ras, G., et al.: Explainable deep learning: a field guide for the uninitiated. J. Artif. Intell. Res. 73, 329\u2013397 (2022)","journal-title":"J. Artif. Intell. Res."},{"issue":"1","key":"43_CR30","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/BF02293851","volume":"47","author":"DB Rubin","year":"1982","unstructured":"Rubin, D.B., et al.: EM algorithms for ML factor analysis. Psychometrika 47(1), 69\u201376 (1982)","journal-title":"Psychometrika"},{"issue":"12","key":"43_CR31","doi-asserted-by":"publisher","first-page":"2290","DOI":"10.1109\/TMI.2012.2216543","volume":"31","author":"MR Sabuncu","year":"2012","unstructured":"Sabuncu, M.R., et al.: The Relevance Voxel Machine (RVoxM): a self-tuning Bayesian model for informative image-based prediction. IEEE Trans. Med. Imaging 31(12), 2290\u20132306 (2012)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"43_CR32","doi-asserted-by":"crossref","unstructured":"Schulz, M.A., et al.: Deep learning for brains?: Different linear and nonlinear scaling in UK biobank brain images vs. machine-learning datasets. BioRxiv p. 757054 (2019)","DOI":"10.1101\/757054"},{"key":"43_CR33","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"43_CR34","unstructured":"Shrikumar, A., et al.: Learning important features through propagating activation differences. In: International Conference on Machine Learning, pp. 3145\u20133153. PMLR (2017)"},{"key":"43_CR35","unstructured":"Simonyan, K., et al.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: Workshop at International Conference on Learning Representations (2014)"},{"key":"43_CR36","unstructured":"Smilkov, D., et al.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)"},{"key":"43_CR37","unstructured":"Springenberg, J.T., et al.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014)"},{"key":"43_CR38","unstructured":"Sundararajan, M., et al.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319\u20133328. PMLR (2017)"},{"key":"43_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1007\/978-3-030-00931-1_62","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"E Varol","year":"2018","unstructured":"Varol, E., Sotiras, A., Zeng, K., Davatzikos, C.: Generative discriminative models for multivariate inference and statistical mapping in medical imaging. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 540\u2013548. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_62"},{"key":"43_CR40","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/978-3-030-32245-8_91","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Q Zhao","year":"2019","unstructured":"Zhao, Q., Adeli, E., Honnorat, N., Leng, T., Pohl, K.M.: Variational AutoEncoder for regression: application to brain aging analysis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 823\u2013831. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_91"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16452-1_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:48:39Z","timestamp":1710244119000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_43","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":"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":"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":"18 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":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}