{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:41:15Z","timestamp":1742913675619,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031443350"},{"type":"electronic","value":"9783031443367"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44336-7_16","type":"book-chapter","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T14:01:39Z","timestamp":1696600899000},"page":"157-166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Breaking Down\u00a0Covariate Shift on\u00a0Pneumothorax Chest X-Ray Classification"],"prefix":"10.1007","author":[{"given":"Bogdan","family":"Bercean","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandru","family":"Buburuzan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreea","family":"Birhala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristian","family":"Avramescu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrei","family":"Tenescu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marius","family":"Marcu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,7]]},"reference":[{"unstructured":"Shifts challenge 2022 - grand challenge. www.shifts.grand-challenge.org\/. Accessed 10 Mar 2023","key":"16_CR1"},{"issue":"8","key":"16_CR2","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1016\/j.jacr.2021.04.002","volume":"18","author":"B Allen","year":"2021","unstructured":"Allen, B., Agarwal, S., Coombs, L., Wald, C., Dreyer, K.: 2020 ACR data science institute artificial intelligence survey. J. Am. Coll. Radiol. 18(8), 1153\u20131159 (2021)","journal-title":"J. Am. Coll. Radiol."},{"doi-asserted-by":"publisher","unstructured":"Aubreville, M., Bertram, C., Breininger, K., Jabari, S., Stathonikos, N., Veta, M.: Mitosis domain generalization challenge 2022 (2022). https:\/\/doi.org\/10.5281\/zenodo.6362337","key":"16_CR3","DOI":"10.5281\/zenodo.6362337"},{"issue":"2","key":"16_CR4","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/TMI.2018.2867350","volume":"38","author":"P Bandi","year":"2018","unstructured":"Bandi, P., et al.: From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge. IEEE Trans. Med. Imaging 38(2), 550\u2013560 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"publisher","unstructured":"European Society of Radiology (ESR). Current practical experience with artificial intelligence in clinical radiology: a survey of the European Society of Radiology. Insights Imaging 13, 107 (2022). https:\/\/doi.org\/10.1186\/s13244-022-01247-y","key":"16_CR5","DOI":"10.1186\/s13244-022-01247-y"},{"key":"16_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101797","volume":"66","author":"A Bustos","year":"2020","unstructured":"Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vay\u00e1, M.: PadChest: a large chest X-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020)","journal-title":"Med. Image Anal."},{"unstructured":"Cohen, J.P., Hashir, M., Brooks, R., Bertrand, H.: On the limits of cross-domain generalization in automated X-ray prediction. In: Medical Imaging with Deep Learning, pp. 136\u2013155. PMLR (2020)","key":"16_CR7"},{"doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","key":"16_CR8","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"6","key":"16_CR9","doi-asserted-by":"publisher","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","volume":"4","author":"JW Gichoya","year":"2022","unstructured":"Gichoya, J.W., et al.: AI recognition of patient race in medical imaging: a modelling study. Lancet Digital Health 4(6), e406\u2013e414 (2022)","journal-title":"Lancet Digital Health"},{"unstructured":"Gulrajani, I., Lopez-Paz, D.: In search of lost domain generalization. In: International Conference on Learning Representations","key":"16_CR10"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","key":"16_CR11","DOI":"10.1109\/CVPR.2017.243"},{"doi-asserted-by":"crossref","unstructured":"Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Thirty-Third AAAI Conference on Artificial Intelligence (2019)","key":"16_CR12","DOI":"10.1609\/aaai.v33i01.3301590"},{"doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: Mimic-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6(1), 317 (2019)","key":"16_CR13","DOI":"10.1038\/s41597-019-0322-0"},{"issue":"1","key":"16_CR14","doi-asserted-by":"publisher","first-page":"21302","DOI":"10.1038\/s41598-022-23990-4","volume":"12","author":"O Kilim","year":"2022","unstructured":"Kilim, O., Olar, A., Jo\u00f3, T., Palicz, T., Pollner, P., Csabai, I.: Physical imaging parameter variation drives domain shift. Sci. Rep. 12(1), 21302 (2022)","journal-title":"Sci. Rep."},{"unstructured":"Koh, P.W., et al.: WILDS: a benchmark of in-the-wild distribution shifts. In: International Conference on Machine Learning, pp. 5637\u20135664. PMLR (2021)","key":"16_CR15"},{"doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5542\u20135550 (2017)","key":"16_CR16","DOI":"10.1109\/ICCV.2017.591"},{"issue":"3","key":"16_CR17","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/BM.2012.031","volume":"22","author":"ML McHugh","year":"2012","unstructured":"McHugh, M.L.: Interrater reliability: the kappa statistic. Biochemia Med. 22(3), 276\u2013282 (2012)","journal-title":"Biochemia Med."},{"key":"16_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/978-3-030-62469-9_7","volume-title":"Thoracic Image Analysis","author":"EHP Pooch","year":"2020","unstructured":"Pooch, E.H.P., Ballester, P., Barros, R.C.: Can we trust deep learning based diagnosis? The impact of domain shift in chest radiograph classification. In: Petersen, J., et al. (eds.) TIA 2020. LNCS, vol. 12502, pp. 74\u201383. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-62469-9_7"},{"issue":"1","key":"16_CR19","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/s41597-022-01608-8","volume":"9","author":"EP Reis","year":"2022","unstructured":"Reis, E.P., et al.: Brax, Brazilian labeled chest X-ray dataset. Sci. Data 9(1), 487 (2022)","journal-title":"Sci. Data"},{"key":"16_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104488","volume":"81","author":"H Wang","year":"2023","unstructured":"Wang, H., Xia, Y.: Domain-ensemble learning with cross-domain mixup for thoracic disease classification in unseen domains. Biomed. Sig. Process. Control 81, 104488 (2023)","journal-title":"Biomed. Sig. Process. Control"},{"unstructured":"Wang, J., et al.: Generalizing to unseen domains: a survey on domain generalization. IEEE Trans. Knowl. Data Eng. 35(8), 8052\u20138072 (2022)","key":"16_CR21"},{"doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097\u20132106 (2017)","key":"16_CR22","DOI":"10.1109\/CVPR.2017.369"},{"unstructured":"Wenkel, S.: Concatenated MNIST (CMNIST). making 784 pixels challenging again. (2019). www.simonwenkel.com\/publications\/articles\/pdf\/20190924_CMNIST.pdf","key":"16_CR23"},{"unstructured":"Yao, H., et al.: Improving out-of-distribution robustness via selective augmentation. In: International Conference on Machine Learning, pp. 25407\u201325437. PMLR (2022)","key":"16_CR24"},{"doi-asserted-by":"crossref","unstructured":"Zhang, H., Dullerud, N., Seyyed-Kalantari, L., Morris, Q., Joshi, S., Ghassemi, M.: An empirical framework for domain generalization in clinical settings. In: Proceedings of the Conference on Health, Inference, and Learning, pp. 279\u2013290 (2021)","key":"16_CR25","DOI":"10.1145\/3450439.3451878"},{"unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)","key":"16_CR26"},{"doi-asserted-by":"crossref","unstructured":"Zhou, K., Liu, Z., Qiao, Y., Xiang, T., Loy, C.C.: Domain generalization: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4396\u20134415 (2022)","key":"16_CR27","DOI":"10.1109\/TPAMI.2022.3195549"},{"doi-asserted-by":"crossref","unstructured":"Zunaed, M., Haque, M., Hasan, T., et al.: Learning to generalize towards unseen domains via a content-aware style invariant framework for disease detection from chest x-rays. arXiv preprint arXiv:2302.13991 (2023)","key":"16_CR28","DOI":"10.1109\/JBHI.2024.3372999"}],"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\/978-3-031-44336-7_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T06:56:52Z","timestamp":1730271412000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44336-7_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031443350","9783031443367"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44336-7_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"7 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vancover, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"unsure2023","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"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}