{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:42:19Z","timestamp":1743072139170,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031732928"},{"type":"electronic","value":"9783031732904"}],"license":[{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T00:00:00Z","timestamp":1729641600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73290-4_14","type":"book-chapter","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T06:02:21Z","timestamp":1729576941000},"page":"138-147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of\u00a0Emerging Infectious Diseases in\u00a0Lung CT Based on\u00a0Spatial Anomaly Patterns"],"prefix":"10.1007","author":[{"given":"Branko","family":"Mitic","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5512-5810","authenticated-orcid":false,"given":"Philipp","family":"Seeb\u00f6ck","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2576-1717","authenticated-orcid":false,"given":"Jennifer","family":"Straub","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6119-6364","authenticated-orcid":false,"given":"Helmut","family":"Prosch","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5536-6873","authenticated-orcid":false,"given":"Georg","family":"Langs","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101860","volume":"67","author":"G Chassagnon","year":"2021","unstructured":"Chassagnon, G., et al.: Ai-driven quantification, staging and outcome prediction of covid-19 pneumonia. Med. Image Anal. 67, 101860 (2021)","journal-title":"Med. Image Anal."},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"23167","DOI":"10.1109\/ACCESS.2022.3153059","volume":"10","author":"A Chharia","year":"2022","unstructured":"Chharia, A., et al.: Deep-precognitive diagnosis: preventing future pandemics by novel disease detection with biologically-inspired conv-fuzzy network. IEEE Access 10, 23167\u201323185 (2022)","journal-title":"IEEE Access"},{"key":"14_CR3","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)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"14_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109009","volume":"127","author":"X Ding","year":"2020","unstructured":"Ding, X., Xu, J., Zhou, J., Long, Q.: Chest CT findings of covid-19 pneumonia by duration of symptoms. Eur. J. Radiol. 127, 109009 (2020)","journal-title":"Eur. J. Radiol."},{"issue":"12","key":"14_CR5","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1093\/cid\/ciaa1654","volume":"72","author":"FC Fang","year":"2021","unstructured":"Fang, F.C., et al.: Covid-19-lessons learned and questions remaining. Clin. Infect. Dis. 72(12), 2225\u20132240 (2021)","journal-title":"Clin. Infect. Dis."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015)","DOI":"10.1167\/16.12.326"},{"key":"14_CR7","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Isola, F., et\u00a0al.: Image-to-image translation with generative adversarial networks. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"14_CR9","unstructured":"Kim, K.S., Oh, S.J., Lee, J.H., Chung, M.J.: 3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: clinical validation on low-dose chest computed tomography. arXiv preprint arXiv:2206.13385 (2022)"},{"key":"14_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"14_CR11","unstructured":"Van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1007\/s10278-020-00413-2","volume":"34","author":"T Nakao","year":"2021","unstructured":"Nakao, T., et al.: Unsupervised deep anomaly detection in chest radiographs. J. Digit. Imaging 34, 418\u2013427 (2021)","journal-title":"J. Digit. Imaging"},{"issue":"3","key":"14_CR13","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1148\/radiol.2020200370","volume":"295","author":"F Pan","year":"2020","unstructured":"Pan, F., et al.: Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (covid-19). Radiology 295(3), 715\u2013721 (2020)","journal-title":"Radiology"},{"key":"14_CR14","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/978-3-031-16452-1_67","volume-title":"MICCAI 2022","author":"WH Pinaya","year":"2022","unstructured":"Pinaya, W.H., et al.: Fast unsupervised brain anomaly detection and segmentation with diffusion models. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13438, pp. 705\u2013714. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_67"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., et al.: Unsupervised brain anomaly detection and segmentation with transformers. arXiv preprint arXiv:2102.11650 (2021)","DOI":"10.1016\/j.media.2022.102475"},{"key":"14_CR16","doi-asserted-by":"publisher","first-page":"A139","DOI":"10.1051\/0004-6361\/202038482","volume":"642","author":"N Porqueres","year":"2020","unstructured":"Porqueres, N., Hahn, O., Jasche, J., Lavaux, G.: A hierarchical field-level inference approach to reconstruction from sparse Lyman-$$\\alpha $$ forest data. Astron. Astrophys. 642, A139 (2020)","journal-title":"Astron. Astrophys."},{"key":"14_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","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"},{"key":"14_CR18","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019)","journal-title":"Med. Image Anal."},{"key":"14_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"},{"issue":"4","key":"14_CR20","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1109\/TMI.2018.2877080","volume":"38","author":"P Seeb\u00f6ck","year":"2018","unstructured":"Seeb\u00f6ck, P., et al.: Unsupervised identification of disease marker candidates in retinal oct imaging data. IEEE Trans. Med. Imaging 38(4), 1037\u20131047 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"14_CR21","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/S1473-3099(20)30086-4","volume":"20","author":"H Shi","year":"2020","unstructured":"Shi, H., et al.: Radiological findings from 81 patients with covid-19 pneumonia in Wuhan, China: a descriptive study. Lancet. Infect. Dis 20(4), 425\u2013434 (2020)","journal-title":"Lancet. Infect. Dis"},{"key":"14_CR22","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"issue":"5","key":"14_CR23","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.crad.2020.03.004","volume":"75","author":"K Wang","year":"2020","unstructured":"Wang, K., Kang, S., Tian, R., Zhang, X., Wang, Y.: Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (covid-19) in the Xiaogan area. Clin. Radiol. 75(5), 341\u2013347 (2020)","journal-title":"Clin. Radiol."},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Wilder-Smith, A., Osman, S.: Public health emergencies of international concern: a historic overview. J. Travel Med. 27(8) (2020)","DOI":"10.1093\/jtm\/taaa227"},{"issue":"8","key":"14_CR25","doi-asserted-by":"publisher","first-page":"2774","DOI":"10.1109\/TEM.2021.3103334","volume":"70","author":"P Yadav","year":"2021","unstructured":"Yadav, P., Menon, N., Ravi, V., Vishvanathan, S.: Lung-GANs: unsupervised representation learning for lung disease classification using chest CT and X-ray images. IEEE Trans. Eng. Manag. 70(8), 2774\u20132786 (2021)","journal-title":"IEEE Trans. Eng. Manag."},{"issue":"6","key":"14_CR26","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1016\/j.cell.2020.04.045","volume":"181","author":"K Zhang","year":"2020","unstructured":"Zhang, K., et al.: Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography. Cell 181(6), 1423\u20131433 (2020)","journal-title":"Cell"},{"key":"14_CR27","doi-asserted-by":"publisher","first-page":"5446","DOI":"10.1007\/s00330-020-06879-6","volume":"30","author":"S Zhou","year":"2020","unstructured":"Zhou, S., Zhu, T., Wang, Y., Xia, L.: Imaging features and evolution on CT in 100 covid-19 pneumonia patients in Wuhan, China. Eur. Radiol. 30, 5446\u20135454 (2020)","journal-title":"Eur. Radiol."},{"key":"14_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101840","volume":"67","author":"Z Zhou","year":"2021","unstructured":"Zhou, Z., Sodha, V., Pang, J., Gotway, M.B., Liang, J.: Models genesis. Med. Image Anal. 67, 101840 (2021)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73290-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T06:05:03Z","timestamp":1729577103000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73290-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,23]]},"ISBN":["9783031732928","9783031732904"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73290-4_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,23]]},"assertion":[{"value":"23 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","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":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}