{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:44:48Z","timestamp":1773215088009,"version":"3.50.1"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031258909","type":"print"},{"value":"9783031258916","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25891-6_40","type":"book-chapter","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T14:03:34Z","timestamp":1678370614000},"page":"525-548","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Brain Structural Saliency over\u00a0the\u00a0Ages"],"prefix":"10.1007","author":[{"given":"Daniel","family":"Taylor","sequence":"first","affiliation":[]},{"given":"Jonathan","family":"Shock","sequence":"additional","affiliation":[]},{"given":"Deshendran","family":"Moodley","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"Ipser","sequence":"additional","affiliation":[]},{"given":"Matthias","family":"Treder","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"40_CR1","unstructured":"Ancona, M., Ceolini, E., \u00d6ztireli, C., Gross, M.: Towards better understanding of gradient-based attribution methods for deep neural networks. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, pp. 1\u201316 (2018)"},{"key":"40_CR2","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1016\/S0047-6374(01)00426-2","volume":"123","author":"BH Anderton","year":"2002","unstructured":"Anderton, B.H.: Ageing of the brain. Mech. Ageing Dev. 123, 811\u2013817 (2002). https:\/\/doi.org\/10.1016\/S0047-6374(01)00426-2","journal-title":"Mech. Ageing Dev."},{"issue":"7","key":"40_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","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), 1\u201346 (2015). https:\/\/doi.org\/10.1371\/journal.pone.0130140","journal-title":"PLoS ONE"},{"key":"40_CR4","doi-asserted-by":"publisher","unstructured":"Cole, J.H., et al.: Brain age predicts mortality. Mol. Psychiatry (2018). https:\/\/doi.org\/10.1038\/mp.2017.62","DOI":"10.1038\/mp.2017.62"},{"key":"40_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neuroimage.2017.07.059","volume":"163","author":"JH Cole","year":"2017","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","journal-title":"Neuroimage"},{"key":"40_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/3-540-48714-X_16","volume-title":"Information Processing in Medical Imaging","author":"D Collins","year":"1999","unstructured":"Collins, D., Zijdenbos, A., Baare, W., Evans, A.: ANIMAL+INSECT: improved cortical structure segmentation. In: Kuba, A., Saamal, M., Todd-Pokropek, A. (eds.) IPMI. LNCS, vol. 1613, pp. 210\u2013223. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/3-540-48714-X_16"},{"key":"40_CR7","doi-asserted-by":"publisher","first-page":"117401","DOI":"10.1016\/J.NEUROIMAGE.2020.117401","volume":"224","author":"NK Dinsdale","year":"2021","unstructured":"Dinsdale, N.K., et al.: Learning patterns of the ageing brain in MRI using deep convolutional networks. Neuroimage 224, 117401 (2021). https:\/\/doi.org\/10.1016\/J.NEUROIMAGE.2020.117401","journal-title":"Neuroimage"},{"key":"40_CR8","unstructured":"Dombrowski, A.K., Alber, M., Anders, C.J., Ackermann, M., M\u00fcller, K.R., Kessel, P.: Explanations can be manipulated and geometry is to blame. In: Advances in Neural Information Processing Systems (2019)"},{"key":"40_CR9","doi-asserted-by":"publisher","first-page":"102003","DOI":"10.1016\/j.nicl.2019.102003","volume":"24","author":"F Eitel","year":"2019","unstructured":"Eitel, F., et al.: Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation. NeuroImage: Clin. 24, 102003 (2019). https:\/\/doi.org\/10.1016\/j.nicl.2019.102003","journal-title":"NeuroImage: Clin."},{"key":"40_CR10","doi-asserted-by":"publisher","first-page":"s102","DOI":"10.1016\/S1053-8119(09)70884-5","volume":"47","author":"V Fonov","year":"2009","unstructured":"Fonov, V., Evans, A., Almli, C., McKinstry, R., 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","journal-title":"Neuroimage"},{"key":"40_CR11","unstructured":"Geirhos, R., Michaelis, C., Wichmann, F.A., Rubisch, P., Bethge, M., Brendel, W.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: 7th International Conference on Learning Representations, ICLR 2019 (c), pp. 1\u201322 (2019)"},{"key":"40_CR12","unstructured":"Grigorescu, I., Cordero-Grande, L., Edwards, A.D., Hajnal, J., Modat, M., Deprez, M.: Interpretable convolutional neural networks for preterm birth classification, pp. 1\u20134 (2019). https:\/\/arxiv.org\/abs\/1910.00071"},{"key":"40_CR13","doi-asserted-by":"publisher","unstructured":"Gunbey, H.P., Ercan, K., Fjndjkoglu, A.S., Bulut, H.T., Karaoglanoglu, M., Arslan, H.: The limbic degradation of aging brain: a quantitative analysis with diffusion tensor. Imaging (2014) https:\/\/doi.org\/10.1155\/2014\/196513","DOI":"10.1155\/2014\/196513"},{"key":"40_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1007\/978-3-030-32248-9_87","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Gupta","year":"2019","unstructured":"Gupta, S., Chan, Y.H., Rajapakse, J.C.: Decoding brain functional connectivity implicated in AD and MCI. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 781\u2013789. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_87"},{"key":"40_CR15","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"40_CR16","doi-asserted-by":"publisher","unstructured":"Hofmann, S.M., et al.: Towards the interpretability of deep learning models for human neuroimaging. BioRxiv (2021). https:\/\/doi.org\/10.1101\/2021.06.25.449906,https:\/\/biorxiv.org\/content\/early\/2021\/08\/26\/2021.06.25.449906.abstract","DOI":"10.1101\/2021.06.25.449906"},{"issue":"2","key":"40_CR17","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1006\/nimg.2002.1132","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson, M., Bannister, P., Brady, J., Smith, S.: Improved optimisation for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2), 825\u2013841 (2002)","journal-title":"Neuroimage"},{"issue":"2","key":"40_CR18","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/S1361-8415(01)00036-6","volume":"5","author":"M Jenkinson","year":"2001","unstructured":"Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143\u2013156 (2001)","journal-title":"Med. Image Anal."},{"issue":"1","key":"40_CR19","doi-asserted-by":"publisher","first-page":"5409","DOI":"10.1038\/s41467-019-13163-9","volume":"10","author":"BA Jonsson","year":"2019","unstructured":"Jonsson, B.A., et al.: Brain age prediction using deep learning uncovers associated sequence variants. Nature Commun. 10(1), 5409 (2019). https:\/\/doi.org\/10.1038\/s41467-019-13163-9","journal-title":"Nature Commun."},{"key":"40_CR20","unstructured":"Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (2014). https:\/\/arxiv.org\/abs\/1412.6980v9"},{"key":"40_CR21","doi-asserted-by":"crossref","unstructured":"Kohlbrenner, M., Bauer, A., Nakajima, S., Binder, A., Samek, W., Lapuschkin, S.: Towards best practice in explaining neural network decisions with LRP, pp. 1\u20135 (2019). https:\/\/arxiv.org\/abs\/1910.09840","DOI":"10.1109\/IJCNN48605.2020.9206975"},{"key":"40_CR22","unstructured":"Kossaifi, J., Kolbeinsson, A., Khanna, A., Furlanello, T., Anandkumar, A.: Tensor regression networks. J. Mach. Learn. Res. 21, 1\u201321 (2020). https:\/\/jmlr.org\/papers\/v21\/18-503.html"},{"issue":"12","key":"40_CR23","doi-asserted-by":"publisher","first-page":"3235","DOI":"10.1002\/HBM.25011\/FORMAT\/PDF","volume":"41","author":"G Levakov","year":"2020","unstructured":"Levakov, G., Rosenthal, G., Shelef, I., Raviv, T.R., Avidan, G.: From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Hum. Brain Mapp. 41(12), 3235\u20133252 (2020). https:\/\/doi.org\/10.1002\/HBM.25011\/FORMAT\/PDF","journal-title":"Hum. Brain Mapp."},{"issue":"6","key":"40_CR24","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/J.ACRA.2007.02.014","volume":"14","author":"J Lutz","year":"2007","unstructured":"Lutz, J., et al.: Evidence of subcortical and cortical aging of the acoustic pathway: a diffusion tensor imaging (DTI) study. Acad. Radiol. 14(6), 692\u2013700 (2007). https:\/\/doi.org\/10.1016\/J.ACRA.2007.02.014","journal-title":"Acad. Radiol."},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"Manera, A., Dadar, M., Fonov, V., Collins, D.: CerebrA, registration and manual label correction of Mindboggle-101 atlas for MNI-ICBM152 template. Sci. Data 7(1), 1\u20139 (2020)","DOI":"10.1038\/s41597-020-0557-9"},{"issue":"2016","key":"40_CR26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","volume":"65","author":"G Montavon","year":"2017","unstructured":"Montavon, G., Lapuschkin, S., Binder, A., Samek, W., M\u00fcller, K.R.: Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recogn. 65(2016), 211\u2013222 (2017). https:\/\/doi.org\/10.1016\/j.patcog.2016.11.008","journal-title":"Pattern Recogn."},{"key":"40_CR27","doi-asserted-by":"publisher","first-page":"101871","DOI":"10.1016\/J.MEDIA.2020.101871","volume":"68","author":"H Peng","year":"2021","unstructured":"Peng, H., Gong, W., Beckmann, C.F., Vedaldi, A., Smith, S.M.: Accurate brain age prediction with lightweight deep neural networks. Med. Image Anal. 68, 101871 (2021). https:\/\/doi.org\/10.1016\/J.MEDIA.2020.101871","journal-title":"Med. Image Anal."},{"key":"40_CR28","doi-asserted-by":"publisher","unstructured":"Peters, R.: Ageing and the brain (2006). https:\/\/doi.org\/10.1136\/pgmj.2005.036665","DOI":"10.1136\/pgmj.2005.036665"},{"key":"40_CR29","unstructured":"Pianpanit, T., et al.: Interpreting deep learning prediction of the Parkinson\u2019s disease diagnosis from SPECT imaging (2019). https:\/\/arxiv.org\/abs\/1908.11199https:\/\/arxiv.org\/abs\/1908.11199"},{"key":"40_CR30","doi-asserted-by":"publisher","unstructured":"Raz, N., Rodrigue, K.M.: Differential aging of the brain: patterns, cognitive correlates and modifiers (2006). https:\/\/doi.org\/10.1016\/j.neubiorev.2006.07.001","DOI":"10.1016\/j.neubiorev.2006.07.001"},{"key":"40_CR31","doi-asserted-by":"publisher","DOI":"10.1186\/s12883-014-0204-1","author":"MA Shafto","year":"2014","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. (2014). https:\/\/doi.org\/10.1186\/s12883-014-0204-1","journal-title":"BMC Neurol."},{"key":"40_CR32","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: 34th International Conference on Machine Learning, ICML, July 2017, pp. 4844\u20134866 (2017)"},{"key":"40_CR33","unstructured":"Shrikumar, A., Greenside, P., Shcherbina, A.Y., Kundaje, A.: Not just a black box : learning important features through propagating activation differences. In: Proceedings of the 33rd International Conference on MachineLearning (2016)"},{"key":"40_CR34","unstructured":"Sixt, L., Granz, M., Landgraf, T.: When explanations lie: why many modified BP attributions fail, December 2019 (2019). https:\/\/arxiv.org\/abs\/1912.09818"},{"key":"40_CR35","unstructured":"Smilkov, D., et al.: SmoothGrad: removing noise by adding noise. arXiv:1706.03825, June 2017. https:\/\/ui.adsabs.harvard.edu\/abs\/2017arXiv170603825S\/abstract"},{"issue":"3","key":"40_CR36","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"S Smith","year":"2002","unstructured":"Smith, S.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143\u2013155 (2002)","journal-title":"Hum. Brain Mapp."},{"issue":"S1","key":"40_CR37","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.neuroimage.2004.07.051","volume":"23","author":"S Smith","year":"2004","unstructured":"Smith, S., et al.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(S1), 208\u201319 (2004)","journal-title":"Neuroimage"},{"key":"40_CR38","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/J.NEUROIMAGE.2019.06.017","volume":"200","author":"SM Smith","year":"2019","unstructured":"Smith, S.M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T.E., Miller, K.L.: Estimation of brain age delta from brain imaging. Neuroimage 200, 528\u2013539 (2019). https:\/\/doi.org\/10.1016\/J.NEUROIMAGE.2019.06.017","journal-title":"Neuroimage"},{"key":"40_CR39","doi-asserted-by":"publisher","DOI":"10.1038\/nn1008","author":"ER Sowell","year":"2003","unstructured":"Sowell, E.R., Peterson, B.S., Thompson, P.M., Welcome, S.E., Henkenius, A.L., Toga, A.W.: Mapping cortical change across the human life span. Nat. Neurosci. (2003). https:\/\/doi.org\/10.1038\/nn1008","journal-title":"Nat. Neurosci."},{"key":"40_CR40","doi-asserted-by":"publisher","unstructured":"Sudlow, C., et al.: UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med. 12(3), e1001779 (2015). https:\/\/doi.org\/10.1371\/JOURNAL.PMED.1001779, https:\/\/journals.plos.org\/plosmedicine\/article?id=10.1371\/journal.pmed.1001779","DOI":"10.1371\/JOURNAL.PMED.1001779"},{"key":"40_CR41","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Gradients of counterfactuals, November 2016. https:\/\/arxiv.org\/abs\/1611.02639"},{"key":"40_CR42","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: 34th International Conference on Machine Learning, ICML, July 2017, pp. 5109\u20135118 (2017)"},{"key":"40_CR43","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/J.NEUROIMAGE.2015.09.018","volume":"144","author":"JR Taylor","year":"2017","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, 262\u2013269 (2017). https:\/\/doi.org\/10.1016\/J.NEUROIMAGE.2015.09.018","journal-title":"Neuroimage"},{"key":"40_CR44","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.neuroimage.2014.01.060","volume":"92","author":"A Winkler","year":"2014","unstructured":"Winkler, A., Ridgway, G., Webster, M., Smith, S., Nichols, T.: Permutation inference for the general linear model. Neuroimage 92, 381\u2013397 (2014)","journal-title":"Neuroimage"},{"key":"40_CR45","doi-asserted-by":"publisher","unstructured":"Zhao, L., Matloff, W., Ning, K., Kim, H., Dinov, I.D., Toga, A.W.: Age-related differences in brain morphology and the modifiers in middle-aged and older adults (2019). https:\/\/doi.org\/10.1093\/cercor\/bhy300, https:\/\/biobank.ctsu.ox","DOI":"10.1093\/cercor\/bhy300"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25891-6_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T10:23:31Z","timestamp":1680690211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25891-6_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031258909","9783031258916"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25891-6_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Certosa di Pontignano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"19 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":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lod2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2022.icas.cc\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","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":"85","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":"38% - 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":"5.6","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":"1.5","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)"}}]}}