{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:43:15Z","timestamp":1772556195691,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031232220","type":"print"},{"value":"9783031232237","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.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\/978-3-031-23223-7_1","type":"book-chapter","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T00:02:40Z","timestamp":1671494560000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Data Poisoning Attack and\u00a0Defenses in\u00a0Connectome-Based Predictive Models"],"prefix":"10.1007","author":[{"given":"Matthew","family":"Rosenblatt","sequence":"first","affiliation":[]},{"given":"Dustin","family":"Scheinost","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.dcn.2018.03.001","volume":"32","author":"BJ Casey","year":"2018","unstructured":"Casey, B.J., et al.: The adolescent brain cognitive development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43\u201354 (2018)","journal-title":"Dev. Cogn. Neurosci."},{"key":"1_CR2","unstructured":"Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Cin\u00e0, A.E., et al.: Wild patterns reloaded: a survey of machine learning security against training data poisoning. arXiv preprint arXiv:2205.01992 (2022)","DOI":"10.1145\/3585385"},{"key":"1_CR4","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neuroimage.2019.02.062","volume":"192","author":"K Dadi","year":"2019","unstructured":"Dadi, K., et al.: Alzheimer\u2019s Disease Neuroimaging Initiative: benchmarking functional connectome-based predictive models for resting-state fMRI. Neuroimage 192, 115\u2013134 (2019)","journal-title":"Neuroimage"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Feng, Y., Ma, B., Zhang, J., Zhao, S., Xia, Y., Tao, D.: FIBA: frequency-Injection based backdoor attack in medical image analysis. arXiv preprint arXiv:2112.01148 (2021)","DOI":"10.1109\/CVPR52688.2022.02021"},{"issue":"6433","key":"1_CR6","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1126\/science.aaw4399","volume":"363","author":"SG Finlayson","year":"2019","unstructured":"Finlayson, S.G., Bowers, J.D., Ito, J., Zittrain, J.L., Beam, A.L., Kohane, I.S.: Adversarial attacks on medical machine learning. Science 363(6433), 1287\u20131289 (2019)","journal-title":"Science"},{"key":"1_CR7","unstructured":"Finlayson, S.G., Chung, H.W., Kohane, I.S., Beam, A.L.: Adversarial attacks against medical deep learning systems. arXiv preprint arXiv:1804.05296 (2018)"},{"key":"1_CR8","unstructured":"Kumar, R.S.S., et al.: Adversarial machine learning - industry perspectives. In: IEEE Symposium on Security and Privacy Workshops (2020)"},{"key":"1_CR9","unstructured":"Marek, S., et al.: Towards reproducible Brain-Wide association studies. bioRxiv preprint bioRxiv:2020.08.21.257758 (2020)"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Matsuo, Y., Takemoto, K.: Backdoor attacks to deep neural network-based system for COVID-19 detection from chest X-ray images. NATO Adv. Sci. Inst. Ser. E Appl. Sci. 11(20), 9556 (2021)","DOI":"10.3390\/app11209556"},{"issue":"2","key":"1_CR11","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/BF02295996","volume":"12","author":"Q McNemar","year":"1947","unstructured":"McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153\u2013157 (1947)","journal-title":"Psychometrika"},{"key":"1_CR12","unstructured":"Nwadike, M., Miyawaki, T., Sarkar, E., Maniatakos, M., Shamout, F.: Explainability matters: backdoor attacks on medical imaging. arXiv preprint arXiv:2101.00008 (2020)"},{"key":"1_CR13","unstructured":"Ortega, P.A., Figueroa, C.J., Ruz, G.A.: A medical claim Fraud\/Abuse detection system based on data mining: a case study in Chile. In: Conference on Data Mining (2006)"},{"key":"1_CR14","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Others: scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"1_CR15","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1177\/1740774512469312","volume":"10","author":"JM Pogue","year":"2013","unstructured":"Pogue, J.M., Devereaux, P.J., Thorlund, K., Yusuf, S.: Central statistical monitoring: detecting fraud in clinical trials. Clin. Trials 10(2), 225\u2013235 (2013)","journal-title":"Clin. Trials"},{"key":"1_CR16","doi-asserted-by":"publisher","unstructured":"Rosenblatt, M., et al.: Can we trust machine learning in fMRI? Simple adversarial attacks break connectome-based predictive models (2021). OSF preprint https:\/\/doi.org\/10.31219\/osf.io\/ptuwe","DOI":"10.31219\/osf.io\/ptuwe"},{"key":"1_CR17","first-page":"1g","volume":"6","author":"WJ Rudman","year":"2009","unstructured":"Rudman, W.J., Eberhardt, J.S., 3rd., Pierce, W., Hart-Hester, S.: Healthcare fraud and abuse. Perspect. Health Inf. Manag. 6, 1g (2009)","journal-title":"Perspect. Health Inf. Manag."},{"key":"1_CR18","unstructured":"Shafahi, A., Huang, W.R., Najibi, M., et al.: Poison frogs! targeted clean-label poisoning attacks on neural networks. In: Advances in Neural Information Processing Systems (2018)"},{"key":"1_CR19","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.neuroimage.2013.05.081","volume":"82","author":"X Shen","year":"2013","unstructured":"Shen, X., Tokoglu, F., Papademetris, X., Constable, R.T.: Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403\u2013415 (2013)","journal-title":"Neuroimage"},{"issue":"3","key":"1_CR20","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1038\/nprot.2016.178","volume":"12","author":"X Shen","year":"2017","unstructured":"Shen, X., et al.: Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat. Protoc. 12(3), 506\u2013518 (2017)","journal-title":"Nat. Protoc."},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"924","DOI":"10.3389\/fpsyt.2019.00924","volume":"10","author":"K Specht","year":"2019","unstructured":"Specht, K.: Current challenges in translational and clinical fMRI and future directions. Front. Psychiatry 10, 924 (2019)","journal-title":"Front. Psychiatry"},{"key":"1_CR22","unstructured":"Steinhardt, K., et al.: Certified defenses for data poisoning attacks. In: Advances in Neural Information Processing Systems (2017)"},{"key":"1_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1007\/978-3-030-58951-6_24","volume-title":"Computer Security \u2013 ESORICS 2020","author":"V Tolpegin","year":"2020","unstructured":"Tolpegin, V., Truex, S., Gursoy, M.E., Liu, L.: Data poisoning attacks against federated learning systems. In: Chen, L., Li, N., Liang, K., Schneider, S. (eds.) ESORICS 2020. LNCS, vol. 12308, pp. 480\u2013501. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58951-6_24"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Wang, B., et al.: Neural cleanse: identifying and mitigating backdoor attacks in neural networks. In: IEEE Symposium on Security and Privacy, pp. 707\u2013723 (2019)","DOI":"10.1109\/SP.2019.00031"},{"issue":"2","key":"1_CR25","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1093\/cercor\/bhz129","volume":"30","author":"S Weis","year":"2020","unstructured":"Weis, S., Patil, K.R., Hoffstaedter, F., Nostro, A., Yeo, B.T.T., Eickhoff, S.B.: Sex classification by resting state brain connectivity. Cereb. Cortex 30(2), 824\u2013835 (2020)","journal-title":"Cereb. Cortex"},{"issue":"14","key":"1_CR26","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1001\/jama.283.14.1858","volume":"283","author":"MK Wynia","year":"2000","unstructured":"Wynia, M.K., Cummins, D.S., VanGeest, J.B., Wilson, I.B.: Physician manipulation of reimbursement rules for patients: between a rock and a hard place. JAMA 283(14), 1858\u20131865 (2000)","journal-title":"JAMA"},{"issue":"11","key":"1_CR27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002683","volume":"15","author":"JR Zech","year":"2018","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., Oermann, E.K.: Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 15(11), e1002683 (2018)","journal-title":"PLoS Med."},{"key":"1_CR28","unstructured":"Zhang, Y., Liang, P.: Defending against whitebox adversarial attacks via randomized discretization. In: Chaudhuri, K., Sugiyama, M. (eds.) Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 89, pp. 684\u2013693. PMLR (2019)"}],"container-title":["Lecture Notes in Computer Science","Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23223-7_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T12:46:51Z","timestamp":1679143611000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23223-7_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031232220","9783031232237"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23223-7_1","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":"20 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on the Ethical and Philosophical Issues in Medical Imaging","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":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epimi2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/epimi\/home","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","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":"5","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":"71% - 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":"2","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)"}}]}}