{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T22:44:21Z","timestamp":1759963461307,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030871987"},{"type":"electronic","value":"9783030871994"}],"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\/978-3-030-87199-4_41","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"434-444","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Scalable, Axiomatic Explanations of Deep Alzheimer\u2019s Diagnosis from Heterogeneous Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1607-7550","authenticated-orcid":false,"given":"Sebastian","family":"P\u00f6lsterl","sequence":"first","affiliation":[]},{"given":"Christina","family":"Aigner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3652-1874","authenticated-orcid":false,"given":"Christian","family":"Wachinger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"41_CR1","unstructured":"Ancona, M., Oztireli, C., Gross, M.: Explaining deep neural networks with a polynomial time algorithm for shapley value approximation. In: Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 272\u2013281 (2019)"},{"key":"41_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012","journal-title":"Inf. Fusion"},{"key":"41_CR3","doi-asserted-by":"publisher","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLOS ONE 10(7), e0130140 (2015). https:\/\/doi.org\/10.1371\/journal.pone.0130140","DOI":"10.1371\/journal.pone.0130140"},{"issue":"1\u20133","key":"41_CR4","doi-asserted-by":"publisher","first-page":"087","DOI":"10.1385\/mn:24:1-3:087","volume":"24","author":"K Blennow","year":"2001","unstructured":"Blennow, K., Vanmechelen, E., Hampel, H.: CSF total tau, A$$\\beta $$42 and phosphorylated tau protein as biomarkers for Alzheimer\u2019s disease. Mol. Neurobiol. 24(1\u20133), 087\u2013098 (2001). https:\/\/doi.org\/10.1385\/mn:24:1-3:087","journal-title":"Mol. Neurobiol."},{"issue":"5","key":"41_CR5","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1016\/j.cor.2008.04.004","volume":"36","author":"J Castro","year":"2009","unstructured":"Castro, J., G\u00f3mez, D., Tejada, J.: Polynomial calculation of the Shapley value based on sampling. Comput. Oper. Res. 36(5), 1726\u20131730 (2009). https:\/\/doi.org\/10.1016\/j.cor.2008.04.004","journal-title":"Comput. Oper. Res."},{"issue":"141","key":"41_CR6","doi-asserted-by":"publisher","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","volume":"15","author":"T Ching","year":"2018","unstructured":"Ching, T., et al.: Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15(141), 20170387 (2018). https:\/\/doi.org\/10.1098\/rsif.2017.0387","journal-title":"J. R. Soc. Interface"},{"key":"41_CR7","unstructured":"Cochran, W.G.: Sampling Techniques, 3rd edn. John Wiley & Sons, Hoboken (1977)"},{"issue":"14","key":"41_CR8","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1016\/j.artint.2008.05.003","volume":"172","author":"SS Fatima","year":"2008","unstructured":"Fatima, S.S., Wooldridge, M., Jennings, N.R.: A linear approximation method for the Shapley value. Artif. Intell. 172(14), 1673\u20131699 (2008). https:\/\/doi.org\/10.1016\/j.artint.2008.05.003","journal-title":"Artif. Intell."},{"issue":"2","key":"41_CR9","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: FreeSurfer. Neuroimage 62(2), 774\u2013781 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2012.01.021","journal-title":"Neuroimage"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Gast, J., Roth, S.: Lightweight probabilistic deep networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3369\u20133378 (2018)","DOI":"10.1109\/CVPR.2018.00355"},{"issue":"9","key":"41_CR11","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1038\/mp.2011.52","volume":"16","author":"E Genin","year":"2011","unstructured":"Genin, E., et al.: APOE and Alzheimer disease: a major gene with semi-dominant inheritance. Mol. Psychiatry 16(9), 903\u2013907 (2011). https:\/\/doi.org\/10.1038\/mp.2011.52","journal-title":"Mol. Psychiatry"},{"key":"41_CR12","doi-asserted-by":"publisher","unstructured":"Guti\u00e9rrez-Becker, B., Wachinger, C.: Deep multi-structural shape analysis: application to neuroanatomy. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 523\u2013531 (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_60","DOI":"10.1007\/978-3-030-00931-1_60"},{"issue":"4","key":"41_CR13","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). https:\/\/doi.org\/10.1002\/jmri.21049","journal-title":"J. Magn. Reson. Imaging"},{"key":"41_CR14","doi-asserted-by":"publisher","unstructured":"Joie, R.L., et al.: Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer\u2019s disease and semantic dementia. NeuroImage Clin. 3, 155\u2013162 (2013). https:\/\/doi.org\/10.1016\/j.nicl.2013.08.007","DOI":"10.1016\/j.nicl.2013.08.007"},{"issue":"4","key":"41_CR15","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K J\u00e1rvelin","year":"2002","unstructured":"J\u00e1rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422\u2013446 (2002). https:\/\/doi.org\/10.1145\/582415.582418","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"41_CR16","unstructured":"Kopper, P., P\u00f6lsterl, S., Wachinger, C., Bischl, B., Bender, A., R\u00fcgamer, D.: Semi-structured deep piecewise exponential models. In: Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, vol. 146, pp. 40\u201353 (2021)"},{"key":"41_CR17","doi-asserted-by":"publisher","unstructured":"Li, X., Dvornek, N.C., Zhuang, J., Ventola, P., Duncan, J.S.: Brain biomarker interpretation in ASD using deep learning and fMRI. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 206\u2013214 (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_24","DOI":"10.1007\/978-3-030-00931-1_24"},{"key":"41_CR18","first-page":"4765","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg, S.M., Lee, S.I.: A Unified Approach to Interpreting Model Predictions. Adv. Neural. Inf. Process. Syst. 30, 4765\u20134774 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"41_CR19","doi-asserted-by":"publisher","unstructured":"Meng, X., D\u2019Arcy, C.: Education and dementia in the context of the cognitive reserve hypothesis: a systematic review with meta-analyses and qualitative analyses. PLoS ONE 7(6), e38268 (2012). https:\/\/doi.org\/10.1371\/journal.pone.0038268","DOI":"10.1371\/journal.pone.0038268"},{"key":"41_CR20","doi-asserted-by":"publisher","unstructured":"P\u00f6lsterl, S., Sarasua, I., Guti\u00e9rrez-Becker, B., Wachinger, C.: A wide and deep neural network for survival analysis from anatomical shape and tabular clinical data. In: Machine Learning and Knowledge Discovery in Databases, pp. 453\u2013464 (2020). https:\/\/doi.org\/10.1007\/978-3-030-43823-4_37","DOI":"10.1007\/978-3-030-43823-4_37"},{"key":"41_CR21","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652\u2013660 (2017)"},{"key":"41_CR22","doi-asserted-by":"publisher","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: The IEEE International Conference on Computer Vision (ICCV) (2017). https:\/\/doi.org\/10.1109\/iccv.2017.74","DOI":"10.1109\/iccv.2017.74"},{"issue":"28","key":"41_CR23","first-page":"307","volume":"2","author":"LS Shapley","year":"1953","unstructured":"Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307\u2013317 (1953)","journal-title":"Contrib. Theory Games"},{"key":"41_CR24","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 3145\u20133153 (2017)"},{"key":"41_CR25","unstructured":"Sundararajan, M., Najmi, A.: The many Shapley values for model explanation. In: Proceedings of the 37th International Conference on Machine Learning, vol. 119, pp. 9269\u20139278 (2020)"},{"key":"41_CR26","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic Attribution for Deep Networks. In: Proc. of the 34th International Conference on Machine Learning. vol. 70, pp. 3319\u20133328 (2017)"},{"key":"41_CR27","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision (ECCV), pp. 818\u2013833 (2014)","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"41_CR28","doi-asserted-by":"publisher","unstructured":"Zhao, G., Zhou, B., Wang, K., Jiang, R., Xu, M.: Respond-CAM: analyzing deep models for 3D imaging data by visualizations. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 485\u2013492 (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_55","DOI":"10.1007\/978-3-030-00928-1_55"},{"key":"41_CR29","doi-asserted-by":"publisher","unstructured":"Zhuang, J., Dvornek, N.C., Li, X., Ventola, P., Duncan, J.S.: Invertible network for classification and biomarker selection for ASD. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 700\u2013708 (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_78","DOI":"10.1007\/978-3-030-32248-9_78"}],"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\/978-3-030-87199-4_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:32:48Z","timestamp":1632378768000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87199-4_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871987","9783030871994"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87199-4_41","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)"}}]}}