{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:09:07Z","timestamp":1790255347114,"version":"4.1.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032380906","type":"print"},{"value":"9783032380913","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-38091-3_29","type":"book-chapter","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:56:30Z","timestamp":1790254590000},"page":"296-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fibers of\u00a0Asymmetric Similarity: A Framework for\u00a0Clinical and\u00a0Imaging Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8636-7986","authenticated-orcid":false,"given":"Johanna P.","family":"M\u00fcller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6252-7658","authenticated-orcid":false,"given":"Matthew","family":"Baugh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0437-7430","authenticated-orcid":false,"given":"Robert","family":"Wright","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8391-7903","authenticated-orcid":false,"given":"Thomas G.","family":"Day","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1065-3008","authenticated-orcid":false,"given":"Reza","family":"Razavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7813-5023","authenticated-orcid":false,"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"issue":"5","key":"29_CR1","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1002\/uog.26094","volume":"61","author":"L Bottelli","year":"2023","unstructured":"Bottelli, L., Franz\u00e8, V., Tuo, G., et al.: Prenatal detection of congenital heart disease at 12\u201313 gestational weeks: detailed analysis of false-negative cases. Ultrasound Obstetrics Gynecol. 61(5), 577\u2013586 (2023). https:\/\/doi.org\/10.1002\/uog.26094","journal-title":"Ultrasound Obstetrics Gynecol."},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Day, T.G., Matthew, J., Budd, S.F., et\u00a0al.: Ai to assist in the fetal anomaly ultrasound scan: A randomized controlled trial. NEJM AI 2(4), AIoa2400747 (2025). https:\/\/doi.org\/10.1056\/AIoa2400747","DOI":"10.1056\/AIoa2400747"},{"issue":"21","key":"29_CR3","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.1161\/01.cir.0000437597.44550.5d","volume":"129","author":"MT Donofrio","year":"2014","unstructured":"Donofrio, M.T., Moon-Grady, A.J., Hornberger, L.K., et al.: Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American Heart Association. Circulation 129(21), 2183\u20132242 (2014). https:\/\/doi.org\/10.1161\/01.cir.0000437597.44550.5d","journal-title":"Circulation"},{"key":"29_CR4","unstructured":"Doumbouya, M.K.B., Jurafsky, D., Manning, C.D.: Tversky neural networks: psychologically plausible deep learning with differentiable tversky similarity arXiv:2506.11035 (2025)"},{"issue":"4","key":"29_CR5","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1109\/TMI.2019.2946059","volume":"39","author":"Y Gong","year":"2020","unstructured":"Gong, Y., Zhang, Y., Zhu, H., et al.: Fetal congenital heart disease echocardiogram screening based on dgacnn: adversarial one-class classification combined with video transfer learning. IEEE Trans. Med. Imaging 39(4), 1206\u20131222 (2020). https:\/\/doi.org\/10.1109\/TMI.2019.2946059","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"29_CR6","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1016\/S0735-1097(02)01886-7","volume":"39","author":"JIE Hoffman","year":"2002","unstructured":"Hoffman, J.I.E., Kaplan, S.: The incidence of congenital heart disease. J. Am. Coll. Cardiol. 39(12), 1890\u20131900 (2002). https:\/\/doi.org\/10.1016\/S0735-1097(02)01886-7","journal-title":"J. Am. Coll. Cardiol."},{"key":"29_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3459943","author":"W Huang","year":"2024","unstructured":"Huang, W., Hu, J., Xiao, J., et al.: Prototype-guided graph reasoning network for few-shot medical image segmentation. IEEE Trans. Med. Imaging (2024). https:\/\/doi.org\/10.1109\/TMI.2024.3459943","journal-title":"IEEE Trans. Med. Imaging"},{"key":"29_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108635","volume":"177","author":"Y Li","year":"2024","unstructured":"Li, Y., Daho, M.E.H., Conze, P.H., et al.: A review of deep learning-based information fusion techniques for multimodal medical image classification. Comput. Biol. Med. 177, 108635 (2024). https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108635","journal-title":"Comput. Biol. Med."},{"key":"29_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106791","volume":"157","author":"ON Manzari","year":"2023","unstructured":"Manzari, O.N., Ahmadabadi, H., et al.: Medvit: a robust vision transformer for generalized medical image classification. Comput. Biol. Med. 157, 106791 (2023). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106791","journal-title":"Comput. Biol. Med."},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"McIntosh-Smith, S., Alam, S.R., Woods, C.: Isambard-AI: a leadership class supercomputer optimised specifically for artificial intelligence (2024)","DOI":"10.1145\/3725789.3725794"},{"key":"29_CR11","unstructured":"Ng, K.S.: Similarity field theory: a mathematical framework for intelligence, arXiv preprint arXiv:2509.18218 (2025)"},{"key":"29_CR12","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., et\u00a0al.: DINOv2: learning robust visual features without supervision. Trans. Mach. Learn. Res. (2024). https:\/\/openreview.net\/forum?id=a68SUt6zFt"},{"key":"29_CR13","unstructured":"Radford, A., Kim, J.W., Hallacy, C., et\u00a0al.: Learning transferable visual models from natural language supervision. In: Meila, M., Zhang, T. (eds.) ICML, 18\u201324 Jul, vol.\u00a0139, pp. 8748\u20138763. PMLR (2021)"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Saha, P., Mishra, D., Hernandez-Cruz, N., et\u00a0al.: Self-supervised normality learning and divergence vector-guided model merging for zero-shot congenital heart disease detection in fetal ultrasound videos. In: Gee, J.C., et\u00a0al. (eds.) MICCAI 2025. pp. 560\u2013571. Springer Nature Switzerland, Cham (2026). https:\/\/doi.org\/10.1007\/978-3-032-04981-0_53","DOI":"10.1007\/978-3-032-04981-0_53"},{"key":"29_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-319-67389-9_44","volume-title":"Machine Learning in Medical Imaging","author":"SSM Salehi","year":"2017","unstructured":"Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 379\u2013387. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67389-9_44"},{"key":"29_CR16","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Guyon, I. (ed.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)"},{"key":"29_CR17","doi-asserted-by":"publisher","unstructured":"Song, Y., Du, X., Zhang, Y., et\u00a0al.: Multi-shot prototype contrastive learning and semantic reasoning for medical image segmentation. In: Greenspan, H., et\u00a0al. (eds.) MICCAI 2023, pp. 578\u2013588. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43901-8_55","DOI":"10.1007\/978-3-031-43901-8_55"},{"key":"29_CR18","doi-asserted-by":"publisher","unstructured":"Stanford, A.: Thyroid ultrasound cine-clip. https:\/\/doi.org\/10.71718\/7m5n-rh16","DOI":"10.71718\/7m5n-rh16"},{"key":"29_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103412","volume":"100","author":"S Tang","year":"2025","unstructured":"Tang, S., Yan, S., Qi, X., et al.: Few-shot medical image segmentation with high-fidelity prototypes. Med. Image Anal. 100, 103412 (2025). https:\/\/doi.org\/10.1016\/j.media.2024.103412","journal-title":"Med. Image Anal."},{"issue":"4","key":"29_CR20","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1037\/0033-295X.84.4.327","volume":"84","author":"A Tversky","year":"1977","unstructured":"Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327 (1977). https:\/\/doi.org\/10.1037\/0033-295X.84.4.327","journal-title":"Psychol. Rev."},{"issue":"2","key":"29_CR21","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1037\/0033-295X.89.2.123","volume":"89","author":"A Tversky","year":"1982","unstructured":"Tversky, A., Gati, I.: Similarity, separability, and the triangle inequality. Psychol. Rev. 89(2), 123 (1982). https:\/\/doi.org\/10.1037\/0033-295X.89.2.123","journal-title":"Psychol. Rev."},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Vega, D., Ceballos, H., Rincon, J.S.V.: CARDIUM: congenital anomaly recognition with diagnostic images and unified medical records. In: Third Workshop on Computer Vision for Automated Medical Diagnosis, arXiv:2510.15208 (2025)","DOI":"10.1109\/ICCVW69036.2025.00129"},{"key":"29_CR23","doi-asserted-by":"publisher","unstructured":"Wang, J., Fan, L., Jing, W., et\u00a0al.: Hypergraph tversky-aware domain incremental learning for brain tumor segmentation with missing modalities. In: Gee, J.C., et\u00a0al. (eds.) MICCAI 2025. pp. 283\u2013293. Springer Nature Switzerland (2026). https:\/\/doi.org\/10.1007\/978-3-032-05141-7_28","DOI":"10.1007\/978-3-032-05141-7_28"},{"key":"29_CR24","doi-asserted-by":"publisher","unstructured":"Yamashita, R., Kapoor, T., Alam, M.N., et\u00a0al.: Toward reduction in false-positive thyroid nodule biopsies with a deep learning\u2013based risk stratification system using us cine-clip images. Radiol. Artifi. Intell. 4(3) (2022). https:\/\/doi.org\/10.1148\/ryai.210174","DOI":"10.1148\/ryai.210174"},{"key":"29_CR25","doi-asserted-by":"publisher","unstructured":"Zhang, S., Xu, Y., Usuyama, N., et\u00a0al.: A multimodal biomedical foundation model trained from fifteen million image\u2013text pairs. NEJM AI 2(1), AIoa2400640 (2025). https:\/\/doi.org\/10.1056\/AIoa2400640","DOI":"10.1056\/AIoa2400640"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38091-3_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:56:33Z","timestamp":1790254593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38091-3_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"ISBN":["9783032380906","9783032380913"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38091-3_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"assertion":[{"value":"25 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}