{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:49:23Z","timestamp":1780447763212,"version":"3.54.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164361","type":"print"},{"value":"9783031164378","type":"electronic"}],"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\/978-3-031-16437-8_37","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"387-397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Penalty Approach for\u00a0Normalizing Feature Distributions to\u00a0Build Confounder-Free Models"],"prefix":"10.1007","author":[{"given":"Anthony","family":"Vento","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6368-0889","authenticated-orcid":false,"given":"Qingyu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Paul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5416-5159","authenticated-orcid":false,"given":"Kilian M.","family":"Pohl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-7763","authenticated-orcid":false,"given":"Ehsan","family":"Adeli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.neuroimage.2018.08.022","volume":"183","author":"E Adeli","year":"2018","unstructured":"Adeli, E., et al.: Chained regularization for identifying brain patterns specific to HIV infection. Neuroimage 183, 425\u2013437 (2018)","journal-title":"Neuroimage"},{"key":"37_CR2","doi-asserted-by":"crossref","unstructured":"Adeli, E., et al.: Deep learning identifies morphological determinants of sex differences in the pre-adolescent brain. Neuroimage, 223, 117293 (2020)","DOI":"10.1016\/j.neuroimage.2020.117293"},{"issue":"98","key":"37_CR3","first-page":"1","volume":"22","author":"A Agarwal","year":"2021","unstructured":"Agarwal, A., Kakade, S.M., Lee, J.D., Mahajan, G.: On the theory of policy gradient methods: optimality, approximation, and distribution shift. J. Mach. Learn. Res. 22(98), 1\u201376 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"37_CR4","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)"},{"key":"37_CR5","unstructured":"Baharlouei, S., Nouiehed, M., Beirami, A., Razaviyayn, M.: R$$\\backslash $$\u2019enyi fair inference. arXiv preprint arXiv:1906.12005 (2019)"},{"key":"37_CR6","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"issue":"6","key":"37_CR7","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1017\/S1355617709990257","volume":"15","author":"L Delano-Wood","year":"2009","unstructured":"Delano-Wood, L., et al.: Heterogeneity in mild cognitive impairment: differences in neuropsychological profile and associated white matter lesion pathology. J. Int. Neuropsychol. Soc. 15(6), 906\u2013914 (2009)","journal-title":"J. Int. Neuropsychol. Soc."},{"key":"37_CR8","doi-asserted-by":"crossref","unstructured":"Deshmukh, S., Khaparde, A.: Faster region-convolutional neural network oriented feature learning with optimal trained recurrent neural network for bone age assessment for pediatrics. Biomed. Signal Process. Control, 71, 103016 (2022)","DOI":"10.1016\/j.bspc.2021.103016"},{"key":"37_CR9","unstructured":"Dosovitskiy, A., et al.: An image is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3d residual networks for action recognition. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 3154\u20133160 (2017)","DOI":"10.1109\/ICCVW.2017.373"},{"key":"37_CR11","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"key":"37_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"37_CR13","unstructured":"Lahiri, A., Alipour, K., Adeli, E., Salimi, B.: Combining counterfactuals with shapley values to explain image models. arXiv preprint arXiv:2206.07087 (2022)"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"37_CR15","unstructured":"Liu, T.Y., Kannan, A., Drake, A., Bertin, M., Wan, N.: Bridging the generalization gap: Training robust models on confounded biological data. arXiv preprint arXiv:1812.04778 (2018)"},{"key":"37_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"814","DOI":"10.1007\/978-3-030-87240-3_78","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"X Liu","year":"2021","unstructured":"Liu, X., Li, B., Bron, E.E., Niessen, W.J., Wolvius, E.B., Roshchupkin, G.V.: Projection-wise disentangling for fair and interpretable representation learning: application to 3D facial shape analysis. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 814\u2013823. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_78"},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Lu, M., et al.: Metadata normalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10917\u201310927 (2021)","DOI":"10.1109\/CVPR46437.2021.01077"},{"issue":"11","key":"37_CR18","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"37_CR19","unstructured":"Neto, E.C.: Causality-aware counterfactual confounding adjustment for feature representations learned by deep models. arXiv preprint arXiv:2004.09466 (2020)"},{"issue":"3","key":"37_CR20","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","volume":"74","author":"RC Petersen","year":"2010","unstructured":"Petersen, R.C., et al.: Alzheimer\u2019s disease neuroimaging initiative (ADNI): clinical characterization. Neurology 74(3), 201\u2013209 (2010)","journal-title":"Neurology"},{"issue":"3","key":"37_CR21","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1214\/aoms\/1177729586","volume":"22","author":"H Robbins","year":"1951","unstructured":"Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22(3), 400\u2013407 (1951)","journal-title":"Ann. Math. Stat."},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Tartaglione, E., Barbano, C.A., Grangetto, M.: End: entangling and disentangling deep representations for bias correction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13508\u201313517 (2021)","DOI":"10.1109\/CVPR46437.2021.01330"},{"key":"37_CR23","unstructured":"Vaswani, A., et al.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Yao, Z., Cao, Y., Lin, Y., Liu, Z., Zhang, Z., Hu, H.: Leveraging batch normalization for vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 413\u2013422 (2021)","DOI":"10.1109\/ICCVW54120.2021.00050"},{"key":"37_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1007\/978-3-030-58610-2_14","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Yong","year":"2020","unstructured":"Yong, H., Huang, J., Meng, D., Hua, X., Zhang, L.: Momentum batch normalization for deep learning with small batch size. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 224\u2013240. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_14"},{"issue":"12","key":"37_CR26","doi-asserted-by":"publisher","first-page":"4523","DOI":"10.1002\/hbm.23326","volume":"37","author":"D Kwon","year":"2016","unstructured":"Kwon, D., et al.: Extracting patterns of morphometry distinguishing HIV associated neurodegeneration from mild cognitive impairment via group cardinality constrained classification. Hum. Brain Mapp. 37(12), 4523\u20134538 (2016)","journal-title":"Hum. Brain Mapp."},{"issue":"1","key":"37_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-19784-9","volume":"11","author":"Q Zhao","year":"2020","unstructured":"Zhao, Q., Adeli, E., Pohl, K.M.: Training confounder-free deep learning models for medical applications. Nat. Commun. 11(1), 1\u20139 (2020)","journal-title":"Nat. Commun."},{"issue":"4","key":"37_CR28","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.jfds.2017.05.001","volume":"2","author":"G Zhong","year":"2016","unstructured":"Zhong, G., Wang, L.N., Ling, X., Dong, J.: An overview on data representation learning: from traditional feature learning to recent deep learning. J. Finan. Data Sci. 2(4), 265\u2013278 (2016)","journal-title":"J. Finan. Data Sci."}],"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\/978-3-031-16437-8_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:07:51Z","timestamp":1710252471000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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)"}}]}}