{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T03:06:49Z","timestamp":1779419209156,"version":"3.53.1"},"publisher-location":"Cham","reference-count":25,"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_19","type":"book-chapter","created":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T12:37:40Z","timestamp":1759063060000},"page":"204-213","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Uncertainty-Aware Classification: A Human-Guided Bayesian Deep Learning Framework"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4656-946X","authenticated-orcid":false,"given":"Peter J. T.","family":"Kampen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9240-7213","authenticated-orcid":false,"given":"Marcel","family":"Reimann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9956-9226","authenticated-orcid":false,"given":"Morten Rieger","family":"Hannemose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3668-3128","authenticated-orcid":false,"given":"Anders Nymark","family":"Christensen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8697-0734","authenticated-orcid":false,"given":"Miriam","family":"Kolko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0068-8170","authenticated-orcid":false,"given":"Anders Bjorholm","family":"Dahl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2872-4660","authenticated-orcid":false,"given":"Josefine Vilsb\u00f8ll","family":"Sundgaard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Bansal, G., Nushi, B., Kamar, E., Horvitz, E., Weld, D.S.: Is the most accurate AI the best teammate? optimizing AI for teamwork. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 11405\u201311414 (2021)","DOI":"10.1609\/aaai.v35i13.17359"},{"issue":"6","key":"19_CR2","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.3390\/diagnostics12061465","volume":"12","author":"J Becker","year":"2022","unstructured":"Becker, J., et al.: Artificial intelligence-based detection of pneumonia in chest radiographs. Diagnostics 12(6), 1465 (2022)","journal-title":"Diagnostics"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Codella, N.C., et\u00a0al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168\u2013172. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"19_CR4","unstructured":"Combalia, M., et\u00a0al.: BCN20000: dermoscopic lesions in the wild. arXiv preprint arXiv:1908.02288 (2019)"},{"key":"19_CR5","unstructured":"Daxberger, E., Kristiadi, A., Immer, A., Eschenhagen, R., Bauer, M., Hennig, P.: Laplace redux-effortless bayesian deep learning. In: Advances in Neural Information Processing Systems, vol. 34, pp. 20089\u201320103 (2021)"},{"key":"19_CR6","unstructured":"Daxberger, E., Nalisnick, E., Allingham, J.U., Antor\u00e1n, J., Hern\u00e1ndez-Lobato, J.M.: Bayesian deep learning via subnetwork inference. In: International Conference on Machine Learning, pp. 2510\u20132521. PMLR (2021)"},{"key":"19_CR7","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"19_CR8","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. arXiv:1706.04599 (2017)"},{"key":"19_CR9","unstructured":"Immer, A., Korzepa, M., Bauer, M.: Improving predictions of bayesian neural nets via local linearization (2021)"},{"issue":"6","key":"19_CR10","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TMI.2022.3141425","volume":"41","author":"L Ju","year":"2022","unstructured":"Ju, L., et al.: Improving medical images classification with label noise using dual-uncertainty estimation. IEEE Trans. Med. Imaging 41(6), 1533\u20131546 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR11","unstructured":"Kampen, P.J., Als, G.R., Andersen, M.R.: Towards scalable bayesian transformers: investigating stochastic subset selection for NLP. In: The 40th Conference on Uncertainty in Artificial Intelligence (2024)"},{"key":"19_CR12","unstructured":"Kerrigan, G., Smyth, P., Steyvers, M.: Combining human predictions with model probabilities via confusion matrices and calibration. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp. 4421\u20134434. Curran Associates, Inc. (2021)"},{"issue":"3","key":"19_CR13","doi-asserted-by":"publisher","first-page":"100300","DOI":"10.1016\/j.xops.2023.100300","volume":"3","author":"HG Lemij","year":"2023","unstructured":"Lemij, H.G., Vente, C., S\u00e1nchez, C.I., Vermeer, K.A.: Characteristics of a large, labeled data set for the training of artificial intelligence for glaucoma screening with fundus photographs. Ophthalmol. Sci. 3(3), 100300 (2023)","journal-title":"Ophthalmol. Sci."},{"key":"19_CR14","unstructured":"Maddox, W., Garipov, T., Izmailov, P., Vetrov, D., Wilson, A.G.: A simple baseline for bayesian uncertainty in deep learning. arXiv:1902.02476 (2019)"},{"key":"19_CR15","doi-asserted-by":"publisher","unstructured":"Maier-Hein, L., et al.: Metrics reloaded: recommendations for image analysis validation. Nat, Methods 21(2), 195\u2013212 (2024). https:\/\/doi.org\/10.1038\/s41592-023-02151-z","DOI":"10.1038\/s41592-023-02151-z"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Narasimhan, H., Jitkrittum, W., Menon, A.K., Rawat, A., Kumar, S.: Post-hoc estimators for learning to defer to an expert. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol.\u00a035, pp. 29292\u201329304. Curran Associates, Inc. (2022)","DOI":"10.52202\/068431-2124"},{"key":"19_CR17","unstructured":"Nixon, J., et al.: Measuring calibration in deep learning. arXiv:1904.01685 (2020)"},{"issue":"1","key":"19_CR18","doi-asserted-by":"publisher","first-page":"14952","DOI":"10.1038\/s41598-022-18751-2","volume":"12","author":"C Reverberi","year":"2022","unstructured":"Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P., Cherubini, A.: Experimental evidence of effective human-AI collaboration in medical decision-making. Sci. Rep. 12(1), 14952 (2022)","journal-title":"Sci. Rep."},{"key":"19_CR19","unstructured":"Sharma, M., Farquhar, S., Nalisnick, E., Rainforth, T.: Do bayesian neural networks need to be fully stochastic? (2023)"},{"issue":"4","key":"19_CR20","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1002\/jvc2.59","volume":"1","author":"NK Ternov","year":"2022","unstructured":"Ternov, N.K., et al.: Generalizability and usefulness of artificial intelligence for skin cancer diagnostics: an algorithm validation study. JEADV Clin. Pract. 1(4), 344\u2013354 (2022)","journal-title":"JEADV Clin. Pract."},{"issue":"7","key":"19_CR21","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/S1470-2045(19)30333-X","volume":"20","author":"P Tschandl","year":"2019","unstructured":"Tschandl, P., et al.: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 20(7), 938\u2013947 (2019)","journal-title":"Lancet Oncol."},{"issue":"1","key":"19_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","journal-title":"Sci. Data"},{"key":"19_CR23","unstructured":"de\u00a0Vente, C., et al.: AIROGS: artificial intelligence for robust glaucoma screening challenge. arXiv:2302.01738 (2023)"},{"key":"19_CR24","unstructured":"Wang, C.: Calibration in deep learning: a survey of the state-of-the-art. arXiv preprint arXiv:2308.01222 (2023)"},{"issue":"7981","key":"19_CR25","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","volume":"622","author":"Y Zhou","year":"2023","unstructured":"Zhou, Y., et al.: A foundation model for generalizable disease detection from retinal images. Nature 622(7981), 156\u2013163 (2023)","journal-title":"Nature"}],"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_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:45:09Z","timestamp":1779417909000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06593-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"ISBN":["9783032065926","9783032065933"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06593-3_19","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 are 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"}}]}}