{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T03:06:54Z","timestamp":1779419214796,"version":"3.53.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032065926","type":"print"},{"value":"9783032065933","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"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\/978-3-032-06593-3_15","type":"book-chapter","created":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T12:37:39Z","timestamp":1759063059000},"page":"158-168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pseudo-D: Informing Multi-view Uncertainty Estimation with\u00a0Calibrated Neural Training Dynamics"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8926-2397","authenticated-orcid":false,"given":"Ang Nan","family":"Gu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Tsang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hooman","family":"Vaseli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Purang","family":"Abolmaesumi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teresa","family":"Tsang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"issue":"2","key":"15_CR1","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/s12574-020-00511-8","volume":"19","author":"Y Abe","year":"2021","unstructured":"Abe, Y.: Screening for aortic stenosis using physical examination and echocardiography. J. Echocardiogr. 19(2), 80\u201385 (2021). https:\/\/doi.org\/10.1007\/s12574-020-00511-8","journal-title":"J. Echocardiogr."},{"issue":"1","key":"15_CR2","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1109\/TMI.2023.3305384","volume":"43","author":"N Ahmadi","year":"2024","unstructured":"Ahmadi, N., Tsang, M., Gu, A., et al.: Transformer-based spatio-temporal analysis for classification of aortic stenosis severity from echocardiography cine series. IEEE Trans. Med. Imaging 43(1), 366\u2013376 (2024)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"15_CR3","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1016\/j.jacc.2006.05.021","volume":"48","author":"RO Bonow","year":"2006","unstructured":"Bonow, R.O., Carabello, B.A., Chatterjee, K., et al.: ACC\/AHA 2006 guidelines for the management of patients with valvular heart disease: a report of the American college of cardiology\/American heart association task force on practice guidelines. J. Am. Coll. Cardiol. 48(3), e1\u2013e148 (2006)","journal-title":"J. Am. Coll. Cardiol."},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Chen, P., Ye, J., Chen, G., et\u00a0al.: Beyond class-conditional assumption: a primary attempt to combat instance-dependent label noise. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 11442\u201311450 (2021)","DOI":"10.1609\/aaai.v35i13.17363"},{"issue":"1","key":"15_CR5","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.echo.2022.10.014","volume":"36","author":"W Dai","year":"2023","unstructured":"Dai, W., Nazzari, H., Namasivayam, M., et al.: Identifying aortic stenosis with a single parasternal long-axis video using deep learning. J. Am. Soc. Echocardiogr. 36(1), 116\u2013118 (2023)","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"15_CR6","unstructured":"DeVries, T., Taylor, G.W.: Learning confidence for out-of-distribution detection in neural networks. arXiv preprint arXiv:1802.04865 (2018)"},{"key":"15_CR7","unstructured":"Feng, L., Ahmed, M.O., Hajimirsadeghi, H., et\u00a0al.: Towards better selective classification. In: International Conference on Learning Representations (2023)"},{"key":"15_CR8","unstructured":"Geifman, Y., El-Yaniv, R.: Selective classification for deep neural networks. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Gu, A.N., Tsang, M., Vaseli, H., et\u00a0al.: Reliable multi-view learning with conformal prediction for aortic stenosis classification in echocardiography. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 327\u2013337. Springer (2024)","DOI":"10.1007\/978-3-031-72378-0_31"},{"key":"15_CR10","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1007\/s00508-015-0904-6","volume":"128","author":"TG Guli\u010d","year":"2016","unstructured":"Guli\u010d, T.G., Makuc, J., Prosen, G., Dinevski, D.: Pocket-size imaging device as a screening tool for aortic stenosis. Wien. Klin. Wochenschr. 128, 348\u2013353 (2016)","journal-title":"Wien. Klin. Wochenschr."},{"key":"15_CR11","unstructured":"Guo, X.: Predicting aortic stenosis severity using deep learning. Ph.D. thesis, Massachusetts Institute of Technology (2021)"},{"issue":"2","key":"15_CR12","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1109\/TPAMI.2022.3171983","volume":"45","author":"Z Han","year":"2022","unstructured":"Han, Z., Zhang, C., Fu, H., Zhou, J.T.: Trusted multi-view classification with dynamic evidential fusion. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 2551\u20132566 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"43","key":"15_CR13","doi-asserted-by":"publisher","first-page":"4592","DOI":"10.1093\/eurheartj\/ehad456","volume":"44","author":"G Holste","year":"2023","unstructured":"Holste, G., Oikonomou, E.K., Mortazavi, B.J., et al.: Severe aortic stenosis detection by deep learning applied to echocardiography. Eur. Heart J. 44(43), 4592\u20134604 (2023)","journal-title":"Eur. Heart J."},{"key":"15_CR14","first-page":"19365","volume":"33","author":"L Huang","year":"2020","unstructured":"Huang, L., Zhang, C., Zhang, H.: Self-adaptive training: beyond empirical risk minimization. Adv. Neural. Inf. Process. Syst. 33, 19365\u201319376 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR15","unstructured":"Huang, Z., Long, G., Wessler, B., Hughes, M.C.: A new semi-supervised learning benchmark for classifying view and diagnosing aortic stenosis from echocardiograms. In: Machine Learning for Healthcare Conference, pp. 614\u2013647. PMLR (2021)"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Huang, Z., Yu, X., Wessler, B.S., Hughes, M.C.: Semi-supervised multimodal multi-instance learning for aortic stenosis diagnosis. arXiv preprint arXiv:2403.06024 (2024)","DOI":"10.1109\/ISBI60581.2025.10981205"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Krishna, H., Desai, K., Slostad, B., et\u00a0al.: Fully automated artificial intelligence assessment of aortic stenosis by echocardiography. J. Am. Soc. Echocardiogr. 36(7), 769\u2013777 (2023). 34th ASE Annual Scientific Sessions","DOI":"10.1016\/j.echo.2023.03.008"},{"key":"15_CR18","unstructured":"Kull, M., Perello\u00a0Nieto, M., K\u00e4ngsepp, M., et\u00a0al.: Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"15_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/978-3-662-44415-3_16","volume-title":"Structural, Syntactic, and Statistical Pattern Recognition","author":"E Morvant","year":"2014","unstructured":"Morvant, E., Habrard, A., Ayache, S.: Majority vote of diverse classifiers for late fusion. In: Fr\u00e4nti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds.) S+SSPR 2014. LNCS, vol. 8621, pp. 153\u2013162. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44415-3_16"},{"key":"15_CR20","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s10554-020-02048-4","volume":"37","author":"O Nemchyna","year":"2021","unstructured":"Nemchyna, O., Soltani, S., Solowjowa, N., et al.: Validity of visual assessment of aortic valve morphology in patients with aortic stenosis using two-dimensional echocardiography. Int. J. Cardiovasc. Imaging 37, 813\u2013823 (2021)","journal-title":"Int. J. Cardiovasc. Imaging"},{"issue":"4","key":"15_CR21","doi-asserted-by":"publisher","first-page":"e25","DOI":"10.1016\/j.jacc.2020.11.018","volume":"77","author":"C Otto","year":"2021","unstructured":"Otto, C., Nishimura, R., Bonow, R., et al.: 2020 ACC\/AHA guideline for the management of patients with valvular heart disease: a report of the American college of cardiology\/American heart association joint committee on clinical practice guidelines. J. Am. Coll. Cardiol. 77(4), e25\u2013e197 (2021)","journal-title":"J. Am. Coll. Cardiol."},{"issue":"3","key":"15_CR22","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.jacc.2011.11.078","volume":"60","author":"P Pibarot","year":"2012","unstructured":"Pibarot, P., Dumesnil, J.G.: Improving assessment of aortic stenosis. J. Am. Coll. Cardiol. 60(3), 169\u2013180 (2012)","journal-title":"J. Am. Coll. Cardiol."},{"key":"15_CR23","unstructured":"Pleiss, G., Zhang, T., Elenberg, E.R., et\u00a0al.: Identifying mislabeled data using the area under the margin ranking. In: Advances in Neural Information Processing Systems (2020)"},{"key":"15_CR24","unstructured":"Rabanser, S., Thudi, A., Hamidieh, K., Dziedzic, A., Papernot, N.: Selective classification via neural network training dynamics. arXiv preprint arXiv:2205.13532 (2022)"},{"issue":"2","key":"15_CR25","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.ijforecast.2013.09.009","volume":"30","author":"VA Satop\u00e4\u00e4","year":"2014","unstructured":"Satop\u00e4\u00e4, V.A., Baron, J., Foster, D.P., et al.: Combining multiple probability predictions using a simple logit model. Int. J. Forecast. 30(2), 344\u2013356 (2014)","journal-title":"Int. J. Forecast."},{"key":"15_CR26","unstructured":"Seedat, N., Crabbe, J., Bica, I., et\u00a0al.: Data-IQ: characterizing subgroups with heterogeneous outcomes in tabular data. In: Advances in Neural Information Processing Systems (2022)"},{"key":"15_CR27","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"issue":"7","key":"15_CR28","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.echo.2015.02.016","volume":"28","author":"JJ Thaden","year":"2015","unstructured":"Thaden, J.J., Nkomo, V.T., Lee, K.J., Oh, J.K.: Doppler imaging in aortic stenosis: the importance of the nonapical imaging windows to determine severity in a contemporary cohort. J. Am. Soc. Echocardiogr. 28(7), 780\u2013785 (2015)","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450\u20136459 (2018)","DOI":"10.1109\/CVPR.2018.00675"},{"key":"15_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103600","volume":"103","author":"H Vaseli","year":"2025","unstructured":"Vaseli, H., Gu, A.N., Tsang, M.Y., et al.: ProtoASNeT: comprehensive evaluation and enhanced performance with uncertainty estimation for aortic stenosis classification in echocardiography. Med. Image Anal. 103, 103600 (2025)","journal-title":"Med. Image Anal."},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Wang, L., Ding, Z., Tao, Z., et\u00a0al.: Generative multi-view human action recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6212\u20136221 (2019)","DOI":"10.1109\/ICCV.2019.00631"},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Wessler, B.S., Huang, Z., Long\u00a0Jr, G., et\u00a0al.: Automated detection of aortic stenosis using machine learning. J. Am. Soc. Echocardiogr. (2023)","DOI":"10.1016\/j.echo.2023.01.006"},{"key":"15_CR33","unstructured":"Zhang, Q., Wu, H., Zhang, C., et\u00a0al.: Provable dynamic fusion for low-quality multimodal data. In: International Conference on Machine Learning, pp. 41753\u201341769. PMLR (2023)"}],"container-title":["Lecture Notes in Computer Science","Uncertainty for Safe Utilization of Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06593-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:46:40Z","timestamp":1779418000000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06593-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"ISBN":["9783032065926","9783032065933"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06593-3_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"assertion":[{"value":"29 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 that\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"UNSURE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejon","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":"27 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":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unsure2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/unsuremiccai.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}