{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T08:09:39Z","timestamp":1771056579710,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723773","type":"print"},{"value":"9783031723780","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72378-0_15","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T07:02:53Z","timestamp":1727852573000},"page":"155-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Ordinal Learning: Longitudinal Attention Alignment Model for\u00a0Predicting Time to\u00a0Future Breast Cancer Events from\u00a0Mammograms"],"prefix":"10.1007","author":[{"given":"Xin","family":"Wang","sequence":"first","affiliation":[]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Marcus","sequence":"additional","affiliation":[]},{"given":"Luyi","family":"Han","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Portaluri","sequence":"additional","affiliation":[]},{"given":"Tianyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chunyao","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Xinglong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Regina","family":"Beets-Tan","sequence":"additional","affiliation":[]},{"given":"Jonas","family":"Teuwen","sequence":"additional","affiliation":[]},{"given":"Ritse","family":"Mann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"issue":"8","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108919","volume":"132","author":"S Dadsetan","year":"2022","unstructured":"Dadsetan, S., Arefan, D., Berg, W.A., Zuley, M.L., Sumkin, J.H., Wu, S.: Deep learning of longitudinal mammogram examinations for breast cancer risk prediction. Pattern Recogn. 132, 108919 (2022)","journal-title":"Pattern Recogn."},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Dong, Q., Du, H., Song, Y., Xu, Y., Liao, J.: Preserving tumor volumes for unsupervised medical image registration. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21208\u201321218 (2023)","DOI":"10.1109\/ICCV51070.2023.01939"},{"issue":"14","key":"15_CR4","doi-asserted-by":"publisher","first-page":"2536","DOI":"10.1200\/JCO.22.01564","volume":"41","author":"M Eriksson","year":"2023","unstructured":"Eriksson, M., Czene, K., Vachon, C., Conant, E.F., Hall, P.: Long-term performance of an image-based short-term risk model for breast cancer. J. Clin. Oncol. 41(14), 2536\u20132545 (2023)","journal-title":"J. Clin. Oncol."},{"issue":"1","key":"15_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13058-022-01509-z","volume":"24","author":"A Gastounioti","year":"2022","unstructured":"Gastounioti, A., Desai, S., Ahluwalia, V.S., Conant, E.F., Kontos, D.: Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer Res. 24(1), 1\u201312 (2022)","journal-title":"Breast Cancer Res."},{"key":"15_CR6","unstructured":"Han, L., et al.: To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection. arXiv preprint arXiv:2401.09336 (2024)"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"15_CR8","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-031-16449-1_21","volume-title":"MICCAI 2022","author":"R Hermoza","year":"2022","unstructured":"Hermoza, R., Maicas, G., Nascimento, J.C., Carneiro, G.: Censor-aware semi-supervised learning for survival time prediction from medical images. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13437, pp. 213\u2013222. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_21"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Jeong, J.J., et\u00a0al.: The emory breast imaging dataset (embed): a racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images. Radiol. Artif. Intell. 5(1), e220047 (2023)","DOI":"10.1148\/ryai.220047"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, J., Park, E., Kim, M., Kim, T., Kooi, T.: Enhancing breast cancer risk prediction by incorporating prior images. arXiv preprint arXiv:2303.15699 (2023)","DOI":"10.1007\/978-3-031-43904-9_38"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Li, W., Huang, X., Lu, J., Feng, J., Zhou, J.: Learning probabilistic ordinal embeddings for uncertainty-aware regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13896\u201313905 (2021)","DOI":"10.1109\/CVPR46437.2021.01368"},{"key":"15_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/978-3-030-59725-2_23","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Azizpour, H., Strand, F., Smith, K.: Decoupling inherent risk and early cancer signs in image-based breast cancer risk models. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part VI. LNCS, vol. 12266, pp. 230\u2013240. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_23"},{"issue":"1","key":"15_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-021-00238-w","volume":"5","author":"K Loizidou","year":"2021","unstructured":"Loizidou, K., Skouroumouni, G., Pitris, C., Nikolaou, C.: Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications. Eur. Radiol. Exp. 5(1), 1\u201312 (2021)","journal-title":"Eur. Radiol. Exp."},{"issue":"12","key":"15_CR14","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1200\/JCO.22.01345","volume":"41","author":"PG Mikhael","year":"2023","unstructured":"Mikhael, P.G., et al.: Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography. J. Clin. Oncol. 41(12), 2191\u20132200 (2023)","journal-title":"J. Clin. Oncol."},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Pan, H., Han, H., Shan, S., Chen, X.: Mean-variance loss for deep age estimation from a face. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5285\u20135294 (2018)","DOI":"10.1109\/CVPR.2018.00554"},{"issue":"10","key":"15_CR16","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1002\/sim.4154","volume":"30","author":"H Uno","year":"2011","unstructured":"Uno, H., Cai, T., Pencina, M.J., D\u2019Agostino, R.B., Wei, L.J.: On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med. 30(10), 1105\u20131117 (2011)","journal-title":"Stat. Med."},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Wang, X., Moriakov, N., Gao, Y., Zhang, T., Han, L., Mann, R.M.: Artificial intelligence in breast imaging. In: Breast Imaging: Diagnosis and Intervention, pp. 435\u2013453 (2022)","DOI":"10.1007\/978-3-030-94918-1_20"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Wang, X., et\u00a0al.: Predicting up to 10 year breast cancer risk using longitudinal mammographic screening history. medRxiv, pp. 2023\u201306 (2023)","DOI":"10.1101\/2023.06.28.23291994"},{"key":"15_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/978-3-030-59722-1_43","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"L Xiao","year":"2020","unstructured":"Xiao, L., et al.: Censoring-aware deep ordinal regression for survival prediction from pathological images. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part V. LNCS, vol. 12265, pp. 449\u2013458. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_43"},{"issue":"12","key":"15_CR20","doi-asserted-by":"publisher","first-page":"6950","DOI":"10.1245\/s10434-023-14144-5","volume":"30","author":"A Yala","year":"2023","unstructured":"Yala, A., Hughes, K.S.: Rethinking risk modeling with machine learning. Ann. Surg. Oncol. 30(12), 6950\u20136952 (2023)","journal-title":"Ann. Surg. Oncol."},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Yala, A., et al.: Toward robust mammography-based models for breast cancer risk. Sci. Transl. Med. 13(578), eaba4373 (2021)","DOI":"10.1126\/scitranslmed.aba4373"},{"key":"15_CR22","unstructured":"Yeoh, H.H., et al.: RADIFUSION: a multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement. arXiv preprint arXiv:2304.00257 (2023)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72378-0_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T07:31:03Z","timestamp":1771054263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72378-0_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723773","9783031723780"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72378-0_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 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":"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":"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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}