{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T07:44:28Z","timestamp":1769759068871,"version":"3.49.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031727863","type":"print"},{"value":"9783031727870","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-72787-0_4","type":"book-chapter","created":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T20:19:24Z","timestamp":1728764364000},"page":"34-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fair and\u00a0Private CT Contrast Agent Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0530-6925","authenticated-orcid":false,"given":"Philipp","family":"Kaess","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3242-0195","authenticated-orcid":false,"given":"Alexander","family":"Ziller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0998-8812","authenticated-orcid":false,"given":"Lea","family":"Mantz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5683-5889","authenticated-orcid":false,"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0119-3903","authenticated-orcid":false,"given":"Florian J.","family":"Fintelmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8382-8062","authenticated-orcid":false,"given":"Georgios","family":"Kaissis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308\u2013318. ACM (2016). https:\/\/doi.org\/10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"issue":"5","key":"4_CR2","doi-asserted-by":"publisher","first-page":"e708","DOI":"10.1097\/SLA.0000000000004040","volume":"275","author":"TD Best","year":"2022","unstructured":"Best, T.D., et al.: Multilevel body composition analysis on chest computed tomography predicts hospital length of stay and complications after lobectomy for lung cancer: a multicenter study. Ann. Surg. 275(5), e708\u2013e715 (2022). https:\/\/doi.org\/10.1097\/SLA.0000000000004040. epub 2020 Jul 8 PMID: 32773626","journal-title":"Ann. Surg."},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Boenisch, F., Dziedzic, A., Schuster, R., Shamsabadi, A.S., Shumailov, I., Papernot, N.: When the curious abandon honesty: federated learning is not private. In: 2023 IEEE 8th European Symposium on Security and Privacy (EuroS &P), pp. 175\u2013199. IEEE (2023)","DOI":"10.1109\/EuroSP57164.2023.00020"},{"key":"4_CR4","unstructured":"Buzaglo, G., et al.: Deconstructing data reconstruction: multiclass, weight decay and general losses. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)"},{"issue":"2","key":"4_CR5","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s10618-010-0190-x","volume":"21","author":"T Calders","year":"2010","unstructured":"Calders, T., Verwer, S.: Three naive Bayes approaches for discrimination-free classification. Data Min. Knowl. Disc. 21(2), 277\u2013292 (2010). https:\/\/doi.org\/10.1007\/s10618-010-0190-x","journal-title":"Data Min. Knowl. Disc."},{"key":"4_CR6","unstructured":"Carlini, N., et al.: Extracting training data from diffusion models. In: 32nd USENIX Security Symposium (USENIX Security 23), pp. 5253\u20135270 (2023)"},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 1\u201313 (2020)","journal-title":"BMC Genomics"},{"issue":"15","key":"4_CR8","doi-asserted-by":"publisher","first-page":"8344","DOI":"10.1073\/pnas.1914598117","volume":"117","author":"A Cohen","year":"2020","unstructured":"Cohen, A., Nissim, K.: Towards formalizing the GDPR\u2019s notion of singling out. Proc. Natl. Acad. Sci. 117(15), 8344\u20138352 (2020)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Cummings, R., Gupta, V., Kimpara, D., Morgenstern, J.: On the compatibility of privacy and fairness, pp. 309-315. UMAP\u201919 Adjunct, Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3314183.3323847","DOI":"10.1145\/3314183.3323847"},{"issue":"1","key":"4_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1111\/rssb.12454","volume":"84","author":"J Dong","year":"2022","unstructured":"Dong, J., Roth, A., Su, W.J.: Gaussian differential privacy. J. R. Stat. Soc. Ser. B Stat Methodol. 84(1), 3\u201337 (2022)","journal-title":"J. R. Stat. Soc. Ser. B Stat Methodol."},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214\u2013226 (2012)","DOI":"10.1145\/2090236.2090255"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Farrand, T., Mireshghallah, F., Singh, S., Trask, A.: Neither private nor fair: impact of data imbalance on utility and fairness in differential privacy (2020)","DOI":"10.1145\/3411501.3419419"},{"key":"4_CR13","unstructured":"Feng, S., Tram\u00e8r, F.: Privacy backdoors: stealing data with corrupted pretrained models. In: International Conference on Machine Learning. PMLR (2024)"},{"key":"4_CR14","unstructured":"Fioretto, F., Tran, C., Hentenryck, P.V.: Decision making with differential privacy under a fairness lens. In: International Joint Conference on Artificial Intelligence (2021). https:\/\/api.semanticscholar.org\/CorpusID:234742410"},{"key":"4_CR15","unstructured":"Fowl, L., Geiping, J., Czaja, W., Goldblum, M., Goldstein, T.: Robbing the fed: directly obtaining private data in federated learning with modified models. In: Tenth International Conference on Learning Representations (2022)"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"G\u00fcld, M., et\u00a0al.: Quality of DICOM header information for image categorization. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 4685 (2002). https:\/\/doi.org\/10.1117\/12.467017","DOI":"10.1117\/12.467017"},{"key":"4_CR17","first-page":"22911","volume":"35","author":"N Haim","year":"2022","unstructured":"Haim, N., Vardi, G., Yehudai, G., Shamir, O., Irani, M.: Reconstructing training data from trained neural networks. Adv. Neural. Inf. Process. Syst. 35, 22911\u201322924 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"4_CR18","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Adv. Neural Inf. Process. Syst. 29, 3315\u20133323 (2016)"},{"key":"4_CR19","unstructured":"Hayes, J., Mahloujifar, S., Balle, B.: Bounding training data reconstruction in DP-SGD. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)"},{"issue":"9","key":"4_CR20","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1038\/s42256-019-0088-2","volume":"1","author":"A Jobin","year":"2019","unstructured":"Jobin, A., Ienca, M., Vayena, E.: The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1(9), 389\u2013399 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"4_CR21","unstructured":"Klause, H., Ziller, A., Rueckert, D., Hammernik, K., Kaissis, G.: Differentially private training of residual networks with scale normalisation. In: Theory and Practice of Differential Privacy Workshop, ICML (2022)"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Lartaud, P.J., Rouchaud, A., Rouet, j.m., Nempont, O., Boussel, L.: Spectral CT Based Training Dataset Generation and Augmentation for Conventional CT Vascular Segmentation, pp. 768\u2013775 (10 2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_85","DOI":"10.1007\/978-3-030-32245-8_85"},{"key":"4_CR23","unstructured":"Massachusetts life sciences center: computational resources and services. https:\/\/www.masslifesciences.com\/"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Matthews, B.W.: Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochim et Biophys. Acta (BBA)-Protein Structure 405(2), 442\u2013451 (1975)","DOI":"10.1016\/0005-2795(75)90109-9"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Nasr, M., Songi, S., Thakurta, A., Papernot, N., Carlini, N.: Adversary instantiation: lower bounds for differentially private machine learning. In: 2021 IEEE Symposium on security and privacy (SP), pp. 866\u2013882. IEEE (2021)","DOI":"10.1109\/SP40001.2021.00069"},{"key":"4_CR26","unstructured":"Sanyal, A., Hu, Y., Yang, F.: How unfair is private learning? In: Cussens, J., Zhang, K. (eds.) Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence. Proceedings of Machine Learning Research, vol.\u00a0180, pp. 1738\u20131748. PMLR (8 2022). https:\/\/proceedings.mlr.press\/v180\/sanyal22a.html"},{"issue":"12","key":"4_CR27","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M.B., Chen, I.Y., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27(12), 2176\u20132182 (2021)","journal-title":"Nat. Med."},{"key":"4_CR28","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1007\/978-3-642-23626-6_21","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2011","author":"M Sofka","year":"2011","unstructured":"Sofka, M., et al.: Automatic contrast phase estimation in CT volumes. In: Fichtinger, G., Martel, A., Peters, T. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2011, pp. 166\u2013174. Springer Berlin Heidelberg, Berlin, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23626-6_21"},{"key":"4_CR29","doi-asserted-by":"publisher","unstructured":"Tayebi\u00a0Arasteh, S., et al.: Preserving fairness and diagnostic accuracy in private large-scale ai models for medical imaging. Commun. Med. 4(1) (Mar 2024). https:\/\/doi.org\/10.1038\/s43856-024-00462-6. http:\/\/dx.doi.org\/10.1038\/s43856-024-00462-6","DOI":"10.1038\/s43856-024-00462-6"},{"key":"4_CR30","doi-asserted-by":"publisher","unstructured":"Ye, Z et al.: Deep learning-based detection of intravenous contrast enhancement on CT scans. Radiol. Artif. Intell. 4(3), e210285 (2022). https:\/\/doi.org\/10.1148\/ryai.210285","DOI":"10.1148\/ryai.210285"},{"key":"4_CR31","unstructured":"Ziller, A., et al.: Reconciling privacy and accuracy in AI for medical imaging. Nat. Mach. Intell. 1\u201311 (2024)"}],"container-title":["Lecture Notes in Computer Science","Ethics and Fairness in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72787-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T20:19:40Z","timestamp":1728764380000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72787-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,13]]},"ISBN":["9783031727863","9783031727870"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72787-0_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,13]]},"assertion":[{"value":"13 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"FAIMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Fairness of AI in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"faimi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/faimi-workshop.github.io\/2024-miccai\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}