{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T05:03:25Z","timestamp":1746594205289,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164514"},{"type":"electronic","value":"9783031164521"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_44","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"459-468","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Interpretable Modeling and\u00a0Reduction of\u00a0Unknown Errors in\u00a0Mechanistic Operators"],"prefix":"10.1007","author":[{"given":"Maryam","family":"Toloubidokhti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nilesh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prashnna K.","family":"Gyawali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian","family":"Zenger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wilson W.","family":"Good","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rob S.","family":"MacLeod","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"3","key":"44_CR1","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/0960-0779(95)00089-5","volume":"7","author":"RR Aliev","year":"1996","unstructured":"Aliev, R.R., Panfilov, A.V.: A simple two-variable model of cardiac excitation. Chaos Solitons Fractals 7(3), 293\u2013301 (1996). https:\/\/doi.org\/10.1016\/0960-0779(95)00089-5","journal-title":"Chaos Solitons Fractals"},{"key":"44_CR2","doi-asserted-by":"publisher","unstructured":"Aras, K., et al.: Experimental data and geometric analysis repository-edgar. J. Electrocardiol. 48 (2015). https:\/\/doi.org\/10.1016\/j.jelectrocard.2015.08.008","DOI":"10.1016\/j.jelectrocard.2015.08.008"},{"key":"44_CR3","doi-asserted-by":"crossref","unstructured":"Cartis, C., Fiala, J., Marteau, B., Roberts, L.: Improving the flexibility and robustness of model-based derivative-free optimization solvers (2018)","DOI":"10.1145\/3338517"},{"key":"44_CR4","doi-asserted-by":"publisher","unstructured":"Chen, E.Z., Chen, T., Sun, S.: MRI image reconstruction via learning optimization using neural ODEs. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 83\u201393. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_9","DOI":"10.1007\/978-3-030-59713-9_9"},{"key":"44_CR5","doi-asserted-by":"publisher","unstructured":"Formaggia, L., Quarteroni, A., Veneziani, A.: Complex Systems in Biomedicine. Springer, Milano (2006). https:\/\/doi.org\/10.1007\/88-470-0396-2","DOI":"10.1007\/88-470-0396-2"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Ghimire, S., Dhamala, J., Gyawali, P., Sapp, J., Horacek, B., Wang, L.: Generative modeling and inverse imaging of cardiac transmembrane potential (2019)","DOI":"10.1007\/978-3-030-00934-2_57"},{"issue":"5","key":"44_CR7","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/51.715491","volume":"17","author":"R Gulrajani","year":"1998","unstructured":"Gulrajani, R.: The forward and inverse problems of electrocardiography. IEEE Eng. Med. Biol. Magaz. 17(5), 84\u2013101 (1998). https:\/\/doi.org\/10.1109\/51.715491","journal-title":"IEEE Eng. Med. Biol. Magaz."},{"issue":"2","key":"44_CR8","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/S0025-5564(97)00024-2","volume":"144","author":"BM Hor\u00e1ek","year":"1997","unstructured":"Hor\u00e1ek, B.M., Clements, J.C.: The inverse problem of electrocardiography: a solution in terms of single- and double-layer sources of the epicardial surface. Math. Biosci. 144(2), 119\u201354 (1997)","journal-title":"Math. Biosci."},{"key":"44_CR9","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.media.2019.03.013","volume":"54","author":"I H\u00e4ggstr\u00f6m","year":"2019","unstructured":"H\u00e4ggstr\u00f6m, I., Schmidtlein, C., Campanella, G., Fuchs, T.: Deeppet: a deep encoder-decoder network for directly solving the pet image reconstruction inverse problem. Med. Image Anal. 54, 253\u2013262 (2019)","journal-title":"Med. Image Anal."},{"key":"44_CR10","unstructured":"Institute, S.: sCIRun: A Scientific Computing Problem Solving Environment, Scientific Computing and Imaging Institute (SCI) (2016). http:\/\/www.scirun.org"},{"issue":"9","key":"44_CR11","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/5.58325","volume":"78","author":"T Kohonen","year":"1990","unstructured":"Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464\u20131480 (1990)","journal-title":"Proc. IEEE"},{"key":"44_CR12","doi-asserted-by":"publisher","unstructured":"Lai, K.-W., Aggarwal, M., van Zijl, P., Li, X., Sulam, J.: Learned proximal networks for quantitative susceptibility mapping. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 125\u2013135. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_13","DOI":"10.1007\/978-3-030-59713-9_13"},{"issue":"1","key":"44_CR13","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MSP.2017.2760358","volume":"35","author":"A Lucas","year":"2018","unstructured":"Lucas, A., Iliadis, M., Molina, R., Katsaggelos, A.K.: Using deep neural networks for inverse problems in imaging: beyond analytical methods. IEEE Signal Process. Magaz. 35(1), 20\u201336 (2018). https:\/\/doi.org\/10.1109\/MSP.2017.2760358","journal-title":"IEEE Signal Process. Magaz."},{"key":"44_CR14","doi-asserted-by":"crossref","unstructured":"Natterer, F., W\u00fcbbeling, F.: Mathematical Methods in Image Reconstruction. SIAM (2001)","DOI":"10.1137\/1.9780898718324"},{"key":"44_CR15","unstructured":"Plonsey, R., Fleming, D.G.: Bioelectric Phenomena. McGraw-Hill (1989)"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Potyagaylo, D., et al.: ECG adapted fastest route algorithm to localize the ectopic excitation origin in CRT patients. Front. Physiol. 10, 183 (2019)","DOI":"10.3389\/fphys.2019.00183"},{"key":"44_CR17","doi-asserted-by":"publisher","unstructured":"Ramanarayanan, S., Murugesan, B., Ram, K., Sivaprakasam, M.: DC-WCNN: a deep cascade of wavelet based convolutional neural networks for MR image reconstruction. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (2020). https:\/\/doi.org\/10.1109\/isbi45749.2020.9098491","DOI":"10.1109\/isbi45749.2020.9098491"},{"key":"44_CR18","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"44_CR19","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1109\/10.476126","volume":"42","author":"R Throne","year":"1995","unstructured":"Throne, R., Olson, L.: The effects of errors in assumed conductivities and geometry on numerical solutions to the inverse problem of electrocardiography. IEEE Trans. Biomed. Eng. 42(12), 1192\u20131200 (1995). https:\/\/doi.org\/10.1109\/10.476126","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"44_CR20","doi-asserted-by":"publisher","unstructured":"Toloubidokhti, M., et al.: Deep adaptive electrocardiographic imaging with generative forward model for error reduction. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds.) FIMH 2021. LNCS, vol. 12738, pp. 471\u2013481. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78710-3_45","DOI":"10.1007\/978-3-030-78710-3_45"},{"key":"44_CR21","unstructured":"Vettigli, G.: Minisom: minimalistic and numpy-based implementation of the self organizing map (2018). https:\/\/github.com\/JustGlowing\/minisom\/"}],"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\/978-3-031-16452-1_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:48:46Z","timestamp":1710244126000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_44","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":"16 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)"}}]}}