{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T12:51:05Z","timestamp":1766407865951,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721168"},{"type":"electronic","value":"9783031721175"}],"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\/978-3-031-72117-5_59","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"633-643","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust Conformal Volume Estimation in\u00a03D Medical Images"],"prefix":"10.1007","author":[{"given":"Benjamin","family":"Lambert","sequence":"first","affiliation":[]},{"given":"Florence","family":"Forbes","sequence":"additional","affiliation":[]},{"given":"Senan","family":"Doyle","sequence":"additional","affiliation":[]},{"given":"Michel","family":"Dojat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"59_CR1","unstructured":"Angelopoulos, A.N., Bates, S.: A gentle introduction to conformal prediction and distribution-free uncertainty quantification. arXiv preprint arXiv:2107.07511 (2021)"},{"key":"59_CR2","unstructured":"Angelopoulos, A.N., Bates, S., Fisch, A., Lei, L., Schuster, T.: Conformal risk control. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"59_CR3","doi-asserted-by":"publisher","unstructured":"Anthony, H., Kamnitsas, K.: On the use of mahalanobis distance for out-of-distribution detection with neural networks for medical imaging. In: Sudre, C.H., Baumgartner, C.F., Dalca, A., Mehta, R., Qin, C., Wells, W.M. (eds.) International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, vol. 14291 pp. 136\u2013146. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-44336-7_14","DOI":"10.1007\/978-3-031-44336-7_14"},{"issue":"2","key":"59_CR4","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1214\/23-AOS2276","volume":"51","author":"RF Barber","year":"2023","unstructured":"Barber, R.F., Candes, E.J., Ramdas, A., Tibshirani, R.J.: Conformal prediction beyond exchangeability. Ann. Stat. 51(2), 816\u2013845 (2023)","journal-title":"Ann. Stat."},{"key":"59_CR5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.clineuro.2016.07.008","volume":"148","author":"MM Baris","year":"2016","unstructured":"Baris, M.M., Celik, A.O., et al.: Role of mass effect, tumor volume and peritumoral edema volume in the differential diagnosis of primary brain tumor and metastasis. Clin. Neurol. Neurosurg. 148, 67\u201371 (2016)","journal-title":"Clin. Neurol. Neurosurg."},{"key":"59_CR6","doi-asserted-by":"crossref","unstructured":"Bickel, S., Br\u00fcckner, M., Scheffer, T.: Discriminative learning for differing training and test distributions. In: Proceedings of the 24th International Conference on Machine Learning, pp. 81\u201388 (2007)","DOI":"10.1145\/1273496.1273507"},{"issue":"4","key":"59_CR7","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TMI.2022.3221898","volume":"42","author":"E Calli","year":"2022","unstructured":"Calli, E., Van Ginneken, B., Sogancioglu, E., Murphy, K.: FRODO: an in-depth analysis of a system to reject outlier samples from a trained neural network. IEEE Trans. Med. Imaging 42(4), 971\u2013981 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"59_CR8","unstructured":"Consortium, T.M.: Project MONAI. https:\/\/doi.org\/10.5281\/zenodo.4323059"},{"key":"59_CR9","doi-asserted-by":"publisher","first-page":"1910","DOI":"10.1109\/TSP.2020.2979601","volume":"68","author":"X Ding","year":"2020","unstructured":"Ding, X., Wang, Z.J., Welch, W.J.: Subsampling generative adversarial networks: density ratio estimation in feature space with SoftPlus loss. IEEE Trans. Signal Process. 68, 1910\u20131922 (2020)","journal-title":"IEEE Trans. Signal Process."},{"key":"59_CR10","doi-asserted-by":"publisher","unstructured":"Futrega, M., Milesi, A., Marcinkiewicz, M., Ribalta, P.: Optimized U-Net for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) International MICCAI Brainlesion Workshop, vol. 12963, pp. 15\u201329. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-031-09002-8_2","DOI":"10.1007\/978-3-031-09002-8_2"},{"issue":"4","key":"59_CR11","doi-asserted-by":"publisher","first-page":"e229178","DOI":"10.1001\/jamanetworkopen.2022.9178","volume":"5","author":"A Ghoneem","year":"2022","unstructured":"Ghoneem, A., Osborne, M.T., et al.: Association of socioeconomic status and infarct volume with functional outcome in patients with ischemic stroke. JAMA Netw. Open 5(4), e229178\u2013e229178 (2022)","journal-title":"JAMA Netw. Open"},{"key":"59_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102596","volume":"82","author":"C Gonz\u00e1lez","year":"2022","unstructured":"Gonz\u00e1lez, C., et al.: Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation. Med. Image Anal. 82, 102596 (2022)","journal-title":"Med. Image Anal."},{"issue":"4","key":"59_CR13","first-page":"5","volume":"3","author":"A Gretton","year":"2009","unstructured":"Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Sch\u00f6lkopf, B., et al.: Covariate shift by kernel mean matching. Dataset Shift Mach. Learn. 3(4), 5 (2009)","journal-title":"Dataset Shift Mach. Learn."},{"key":"59_CR14","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321\u20131330. PMLR (2017)"},{"key":"59_CR15","first-page":"1391","volume":"10","author":"T Kanamori","year":"2009","unstructured":"Kanamori, T., Hido, S., Sugiyama, M.: A least-squares approach to direct importance estimation. J. Mach. Learn. Res. 10, 1391\u20131445 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"59_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"59_CR17","unstructured":"LaBella, D., et\u00a0al.: The ASNR-MICCAI brain tumor segmentation (BraTS) challenge 2023: intracranial meningioma. arXiv preprint arXiv:2305.07642 (2023)"},{"key":"59_CR18","doi-asserted-by":"publisher","unstructured":"Lambert, B., Forbes, F., Doyle, S., Dojat, M.: TriadNet: sampling-free predictive intervals for Lesional volume in 3D brain MR images. In: Sudre, C.H., Baumgartner, C.F., Dalca, A., Mehta, R., Qin, C., Wells, W.M. (eds.) International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, vol. 14291, pp. 32\u201341. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-44336-7_4","DOI":"10.1007\/978-3-031-44336-7_4"},{"key":"59_CR19","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10472-013-9366-6","volume":"74","author":"J Lei","year":"2015","unstructured":"Lei, J., Rinaldo, A., Wasserman, L.: A conformal prediction approach to explore functional data. Ann. Math. Artif. Intell. 74, 29\u201343 (2015)","journal-title":"Ann. Math. Artif. Intell."},{"key":"59_CR20","doi-asserted-by":"crossref","unstructured":"Mattiesing, R.M., Gentile, G., et\u00a0al.: The spatio-temporal relationship between white matter lesion volume changes and brain atrophy in clinically isolated syndrome and early multiple sclerosis. NeuroImage: Clin. 36, 103220 (2022)","DOI":"10.1016\/j.nicl.2022.103220"},{"issue":"10","key":"59_CR21","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"59_CR22","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1587\/transinf.2014EDP7335","volume":"98","author":"H Nam","year":"2015","unstructured":"Nam, H., Sugiyama, M.: Direct density ratio estimation with convolutional neural networks with application in outlier detection. IEICE Trans. Inf. Syst. 98(5), 1073\u20131079 (2015)","journal-title":"IEICE Trans. Inf. Syst."},{"key":"59_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/3-540-36755-1_29","volume-title":"Machine Learning: ECML 2002","author":"H Papadopoulos","year":"2002","unstructured":"Papadopoulos, H., Proedrou, K., Vovk, V., Gammerman, A.: Inductive confidence machines for regression. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 345\u2013356. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-36755-1_29"},{"key":"59_CR24","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":"59_CR25","unstructured":"Sugiyama, M., Suzuki, T., Kanamori, T.: Density ratio estimation: a comprehensive review (statistical experiment and its related topics). 1703, 10\u201331 (2010)"},{"key":"59_CR26","unstructured":"Tibshirani, R.J., Foygel\u00a0Barber, R., Candes, E., Ramdas, A.: Conformal prediction under covariate shift. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"59_CR27","doi-asserted-by":"publisher","unstructured":"Woodland, M., et al.: Dimensionality reduction for improving out-of-distribution detection in medical image segmentation. In: Sudre, C.H., Baumgartner, C.F., Dalca, A., Mehta, R., Qin, C., Wells, W.M. (eds.) International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, vol. 14291, pp. 147\u2013156. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-44336-7_15","DOI":"10.1007\/978-3-031-44336-7_15"},{"issue":"6","key":"59_CR28","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.3390\/diagnostics13061068","volume":"13","author":"Z Xue","year":"2023","unstructured":"Xue, Z., Yang, F., Rajaraman, S., Zamzmi, G., Antani, S.: Cross dataset analysis of domain shift in CXR lung region detection. Diagnostics 13(6), 1068 (2023)","journal-title":"Diagnostics"}],"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\/978-3-031-72117-5_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:19:58Z","timestamp":1727871598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72117-5_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721168","9783031721175"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72117-5_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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":"BL and SD are employees of the Pixyl Company. MD and FF serve on Pixyl advisory board.","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"}}]}}