{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:47:05Z","timestamp":1758934025651,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164309"},{"type":"electronic","value":"9783031164316"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16431-6_56","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T21:02:58Z","timestamp":1663189378000},"page":"592-601","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Reinforcement Learning for\u00a0Active Modality Selection During Diagnosis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8741-2566","authenticated-orcid":false,"given":"Gabriel","family":"Bernardino","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5756-7847","authenticated-orcid":false,"given":"Anders","family":"Jonsson","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7865-1108","authenticated-orcid":false,"given":"Filip","family":"Loncaric","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0252-5831","authenticated-orcid":false,"given":"Pablo-Miki","family":"Mart\u00ed Castellote","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1300-4732","authenticated-orcid":false,"given":"Marta","family":"Sitges","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5495-7655","authenticated-orcid":false,"given":"Patrick","family":"Clarysse","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8803-2004","authenticated-orcid":false,"given":"Nicolas","family":"Duchateau","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"56_CR1","doi-asserted-by":"crossref","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","DOI":"10.1109\/TMI.2018.2837502"},{"key":"56_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/978-3-030-93722-5_12","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge","author":"G Bernardino","year":"2022","unstructured":"Bernardino, G., et al.: Hierarchical multi-modality prediction model to\u00a0assess obesity-related remodelling. In: Puyol Ant\u00f3n, E., Pop, M., Mart\u00edn-Isla, C., Sermesant, M., Suinesiaputra, A., Camara, O., Lekadir, K., Young, A. (eds.) STACOM 2021. LNCS, vol. 13131, pp. 103\u2013112. Springer, Cham (2022)"},{"key":"56_CR3","doi-asserted-by":"crossref","unstructured":"Detrano, R., et al.: International application of a new probability algorithm for the diagnosis of coronary artery disease. Am. J. Cardiol. 64(5), 304\u2013310 (1989)","DOI":"10.1016\/0002-9149(89)90524-9"},{"key":"56_CR4","doi-asserted-by":"crossref","unstructured":"Garbi, M., et al.: EACVI appropriateness criteria for the use of cardiovascular imaging in heart failure derived from European National Imaging Societies voting. Europ. Heart J. Cardiovascular Imaging 17(7), 711\u2013721 (2016)","DOI":"10.1093\/ehjci\/jew081"},{"key":"56_CR5","unstructured":"Gong, W., et al.: Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)"},{"key":"56_CR6","doi-asserted-by":"publisher","first-page":"10","DOI":"10.4103\/jehp.jehp_225_20","volume":"10","author":"M Hadian","year":"2021","unstructured":"Hadian, M., Jabbari, A., Mazaheri, E., Norouzi, M.: What is the impact of clinical guidelines on imaging costs? J. Educ. Health Promotion 10, 10 (2021)","journal-title":"J. Educ. Health Promotion"},{"key":"56_CR7","doi-asserted-by":"crossref","unstructured":"Loncaric, F., et al.: Automated pattern recognition in whole-cardiac cycle echocardiographic data: capturing functional phenotypes with machine learning. J. Am. Soc. Echocardiography 34(11), 1170\u20131183 (2021)","DOI":"10.1016\/j.echo.2021.06.014"},{"key":"56_CR8","doi-asserted-by":"crossref","unstructured":"Nagueh, S.F., et al.: Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American society of echocardiography and the European Association of Cardiovascular Imaging. J. Am. Soc. Echocardiography : official publication of the American Society of Echocardiography 29(4), 277\u2013314 (2016)","DOI":"10.1016\/j.echo.2016.01.011"},{"key":"56_CR9","unstructured":"Ng, A.Y.: Feature selection, L1 vs. L2 regularization, and rotational invariance. In: Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 (2004)"},{"key":"56_CR10","unstructured":"Wang, J., Trapeznikov, K., Saligrama, V.: Efficient learning by directed acyclic graph for resource constrained prediction. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc. (2015)"},{"key":"56_CR11","unstructured":"Xu, Z.E., Kusner, M.J., Weinberger, K.Q., Chen, M., Chapelle, O.: Classifier cascades and trees for minimizing feature evaluation cost. J. Mach. Learn. Res. 15, 2113\u20132144 (2014)"},{"key":"56_CR12","unstructured":"Yin, H., Li, Y., Pan, S.J., Zhang, C., Tschiatschek, S.: Reinforcement Learning with Efficient Active Feature Acquisition. arXiv, September 2020"},{"key":"56_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, T., Ruan, S., Canu, S.: A review: deep learning for medical image segmentation using multi-modality fusion. Array 3\u20134, 100004 (2019)","DOI":"10.1016\/j.array.2019.100004"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16431-6_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:54:13Z","timestamp":1710356053000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16431-6_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164309","9783031164316"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16431-6_56","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}