{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:17:52Z","timestamp":1758359872479,"version":"3.44.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049834"},{"type":"electronic","value":"9783032049841"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04984-1_52","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:25:04Z","timestamp":1758299104000},"page":"541-550","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic Ground Truth Counterfactuals for\u00a0Comprehensive Evaluation of\u00a0Causal Generative Models in\u00a0Medical Imaging"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7802-6820","authenticated-orcid":false,"given":"Emma A. M.","family":"Stanley","sequence":"first","affiliation":[]},{"given":"Vibujithan","family":"Vigneshwaran","sequence":"additional","affiliation":[]},{"given":"Erik Y.","family":"Ohara","sequence":"additional","affiliation":[]},{"given":"Finn G.","family":"Vamosi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2556-3224","authenticated-orcid":false,"given":"Nils D.","family":"Forkert","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8845-360X","authenticated-orcid":false,"given":"Matthias","family":"Wilms","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"52_CR1","unstructured":"Castro, D.C., Tan, J., Kainz, B., Konukoglu, E., Glocker, B.: Morpho-MNIST: quantitative assessment and diagnostics for representation learning. J. Mach. Learn. Res. 20(178) (2019)"},{"issue":"1","key":"52_CR2","doi-asserted-by":"publisher","first-page":"3673","DOI":"10.1038\/s41467-020-17478-w","volume":"11","author":"DC Castro","year":"2020","unstructured":"Castro, D.C., Walker, I., Glocker, B.: Causality matters in medical imaging. Nat. Commun. 11(1), 3673 (2020)","journal-title":"Nat. Commun."},{"issue":"16","key":"52_CR3","doi-asserted-by":"publisher","first-page":"5278","DOI":"10.1002\/hbm.25615","volume":"42","author":"N Maikusa","year":"2021","unstructured":"Maikusa, N., et al.: Comparison of traveling-subject and ComBat harmonization methods for assessing structural brain characteristics. Hum. Brain Mapp. 42(16), 5278\u20135287 (2021)","journal-title":"Hum. Brain Mapp."},{"key":"52_CR4","unstructured":"Melistas, T., et al.: Benchmarking counterfactual image generation. arXiv preprint arXiv:2403.20287 (2024)"},{"key":"52_CR5","unstructured":"Monteiro, M., Ribeiro, F.D.S., Pawlowski, N., Castro, D.C., Glocker, B.: Measuring axiomatic soundness of counterfactual image models. arXiv preprint arXiv:2303.01274 (2023)"},{"key":"52_CR6","doi-asserted-by":"crossref","unstructured":"Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607\u2013617 (2020)","DOI":"10.1145\/3351095.3372850"},{"key":"52_CR7","unstructured":"Pawlowski, N., Coelho\u00a0de Castro, D., Glocker, B.: Deep structural causal models for tractable counterfactual inference. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 857\u2013869. Curran Associates, Inc. (2020)"},{"key":"52_CR8","doi-asserted-by":"crossref","unstructured":"Pearl, J.: The Causal Foundations of Structural Equation Modeling. Technical report, Defense Technical Information Center, Fort Belvoir, VA (2012)","DOI":"10.21236\/ADA557445"},{"key":"52_CR9","doi-asserted-by":"publisher","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)","journal-title":"Med. Image Anal."},{"key":"52_CR10","unstructured":"Ribeiro, F.D.S., Xia, T., Monteiro, M., Pawlowski, N., Glocker, B.: High fidelity image counterfactuals with probabilistic causal models. In: Proceedings of the 40th International Conference on Machine Learning, pp. 7390\u20137425. PMLR (2023). ISSN: 2640-3498"},{"key":"52_CR11","unstructured":"Ribeiro, F.D.S., Xia, T., Monteiro, M., Pawlowski, N., Glocker, B.: High fidelity image counterfactuals with probabilistic causal models. arXiv preprint arXiv:2306.15764 (2023)"},{"key":"52_CR12","unstructured":"Sanchez, P., Tsaftaris, S.A.: Diffusion causal models for counterfactual estimation. arXiv preprint arXiv:2202.10166 (2022)"},{"issue":"8","key":"52_CR13","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.220638","volume":"9","author":"P Sanchez","year":"2022","unstructured":"Sanchez, P., Voisey, J.P., Xia, T., Watson, H.I., O\u2019Neil, A.Q., Tsaftaris, S.A.: Causal machine learning for healthcare and precision medicine. R. Soc. Open Sci. 9(8), 220638 (2022)","journal-title":"R. Soc. Open Sci."},{"key":"52_CR14","doi-asserted-by":"crossref","unstructured":"Stanley, E.A.M., Souza, R., Wilms, M., Forkert, N.D.: Where, why, and how is bias learned in medical image analysis models? A study of bias encoding within convolutional networks using synthetic data. eBioMedicine 111, 105501 (2025)","DOI":"10.1016\/j.ebiom.2024.105501"},{"key":"52_CR15","unstructured":"Stanley, E.A.M., et al.: Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging. J. Am. Med. Inform. Assoc. ocae165 (2024)"},{"key":"52_CR16","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/978-3-031-43895-0_46","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2023","author":"EAM Stanley","year":"2023","unstructured":"Stanley, E.A.M., Wilms, M., Forkert, N.D.: A flexible framework for simulating and evaluating biases in deep learning-based medical image analysis. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, pp. 489\u2013499. Springer, Cham (2023)"},{"key":"52_CR17","unstructured":"Vigneshwaran, V., Ohara, E., Wilms, M., Forkert, N.: Macaw: a causal generative model for medical imaging. arXiv preprint arXiv:2412.02900 (2024)"},{"key":"52_CR18","unstructured":"Xia, K., Pan, Y., Bareinboim, E.: Neural causal models for counterfactual identification and estimation. arXiv preprint arXiv:2210.00035 (2022)"},{"key":"52_CR19","doi-asserted-by":"crossref","unstructured":"Xia, T., Roschewitz, M., De\u00a0Sousa\u00a0Ribeiro, F., Jones, C., Glocker, B.: Mitigating attribute amplification in counterfactual image generation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 546\u2013556. Springer (2024)","DOI":"10.1007\/978-3-031-72117-5_51"},{"key":"52_CR20","doi-asserted-by":"publisher","first-page":"1039160","DOI":"10.3389\/fradi.2022.1039160","volume":"2","author":"T Xia","year":"2022","unstructured":"Xia, T., Sanchez, P., Qin, C., Tsaftaris, S.A.: Adversarial counterfactual augmentation: application in Alzheimer\u2019s disease classification. Front. Radiol. 2, 1039160 (2022)","journal-title":"Front. Radiol."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04984-1_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:25:11Z","timestamp":1758299111000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04984-1_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049834","9783032049841"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04984-1_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}