{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:07:09Z","timestamp":1759334829327,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030593537"},{"type":"electronic","value":"9783030593544"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59354-4_4","type":"book-chapter","created":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T19:05:43Z","timestamp":1601492743000},"page":"35-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Learned Deep Radiomics for Survival Analysis with Attention"],"prefix":"10.1007","author":[{"given":"Ludivine","family":"Morvan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristina","family":"Nanni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne-Victoire","family":"Michaud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bastien","family":"Jamet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cl\u00e9ment","family":"Bailly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caroline","family":"Bodet-Milin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephane","family":"Chauvie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cyrille","family":"Touzeau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philippe","family":"Moreau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Zamagni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francoise","family":"Kraeber-Bod\u00e9r\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Carlier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana","family":"Mateus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Zhu, F., Huang, J.: WSISA: making survival prediction from whole slide histopathological images. In: CVPR, pp. 970\u2013975 (2017)","DOI":"10.1109\/CVPR.2017.725"},{"issue":"2","key":"4_CR2","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/TRPMS.2019.2896399","volume":"3","author":"A Amyar","year":"2019","unstructured":"Amyar, A., Ruan, S., Gardin, I., Chatelain, C., Decazes, P., Modzelewski, R.: 3-D RPET-NET: development of a 3-D pet imaging convolutional neural network for radiomics analysis and outcome prediction. IEEE Trans. Radiat. Plasma Med. Sci. 3(2), 225\u2013231 (2019)","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"issue":"4","key":"4_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1006076","volume":"14","author":"T Ching","year":"2018","unstructured":"Ching, T., Zhu, X., Garmire, L.X.: Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Comput. Biol. 14(4), 1\u201318 (2018). https:\/\/doi.org\/10.1371\/journal.pcbi.1006076","journal-title":"PLoS Comput. Biol."},{"issue":"3","key":"4_CR4","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s40336-014-0064-0","volume":"2","author":"GJR Cook","year":"2014","unstructured":"Cook, G.J.R., Siddique, M., Taylor, B.P., Yip, C., Chicklore, S., Goh, V.: Radiomics in PET: principles and applications. Clin. Transl. Imaging 2(3), 269\u2013276 (2014). https:\/\/doi.org\/10.1007\/s40336-014-0064-0","journal-title":"Clin. Transl. Imaging"},{"issue":"1","key":"4_CR5","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1002\/sim.4780140108","volume":"14","author":"D Faraggi","year":"1995","unstructured":"Faraggi, D., Simon, R.: A neural network model for survival data. Stat. Med. 14(1), 73\u201382 (1995). https:\/\/doi.org\/10.1002\/sim.4780140108","journal-title":"Stat. Med."},{"issue":"1","key":"4_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/peerj.6257","volume":"2019","author":"MF Gensheimer","year":"2019","unstructured":"Gensheimer, M.F., Narasimhan, B.: A scalable discrete-time survival model for neural networks. PeerJ 2019(1), 1\u201317 (2019). https:\/\/doi.org\/10.7717\/peerj.6257","journal-title":"PeerJ"},{"key":"4_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-319-10578-9_23","volume-title":"Computer Vision \u2013 ECCV 2014","author":"K He","year":"2014","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346\u2013361. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_23"},{"issue":"4","key":"4_CR8","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.diii.2019.02.008","volume":"100","author":"P Herent","year":"2019","unstructured":"Herent, P., et al.: Detection and characterization of MRI breast lesions using deep learning. Diagn. Interv. Imaging 100(4), 219\u2013225 (2019). https:\/\/doi.org\/10.1016\/j.diii.2019.02.008","journal-title":"Diagn. Interv. Imaging"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Huang, Q., Yang, D., Wu, P., Qu, H., Yi, J., Metaxas, D.: MRI reconstruction via cascaded channel-wise attention network. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1622\u20131626 (2019). https:\/\/doi.org\/10.1109\/ISBI.2019.8759423","DOI":"10.1109\/ISBI.2019.8759423"},{"issue":"2","key":"4_CR10","doi-asserted-by":"publisher","first-page":"e0211057","DOI":"10.1371\/journal.pone.0211057","volume":"14","author":"DA Kaji","year":"2019","unstructured":"Kaji, D.A., Zech, J.R., Kim, J.S., et al.: An attention based deep learning model of clinical events in the intensive care unit. PLoS ONE 14(2), e0211057 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0211057","journal-title":"PLoS ONE"},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18, 24 (2018). https:\/\/doi.org\/10.1186\/s12874-018-0482-1","journal-title":"BMC Med. Res. Methodol."},{"key":"4_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-6646-9","volume-title":"Survival Analysis: A Self-Learning Text","year":"2012","unstructured":"Kleinbaum, D.G., Klein, M. (eds.): Survival Analysis: A Self-Learning Text. Springer, New York (2012). https:\/\/doi.org\/10.1007\/978-1-4419-6646-9"},{"key":"4_CR13","doi-asserted-by":"publisher","unstructured":"Lao, J., et al.: A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci. Rep. 7(1) (2017). https:\/\/doi.org\/10.1038\/s41598-017-10649-8","DOI":"10.1038\/s41598-017-10649-8"},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Li, H., et al.: Deep convolutional neural networks for imaging data based survival analysis of rectal cancer. In: ISBI, April, vol. 2019, pp. 846\u2013849 (2019). https:\/\/doi.org\/10.1109\/ISBI.2019.8759301","DOI":"10.1109\/ISBI.2019.8759301"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Sun, Q., Bai, H., Liang, C., Chen, Y., Li, Z.: 3D deep attention network for survival prediction from magnetic resonance images in glioblastoma. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1381\u20131384 (2019)","DOI":"10.1109\/ICIP.2019.8803077"},{"issue":"13","key":"4_CR16","doi-asserted-by":"publisher","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","volume":"115","author":"P Mobadersany","year":"2018","unstructured":"Mobadersany, P., et al.: Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl. Acad. Sci. 115(13), E2970\u2013E2979 (2018). https:\/\/doi.org\/10.1073\/pnas.1717139115. https:\/\/www.pnas.org\/content\/115\/13\/E2970","journal-title":"Proc. Natl. Acad. Sci."},{"key":"4_CR17","doi-asserted-by":"publisher","first-page":"966","DOI":"10.3389\/fnins.2019.00966","volume":"13","author":"ZA Shboul","year":"2019","unstructured":"Shboul, Z.A., Alam, M., Vidyaratne, L., Pei, L., Elbakary, M.I., Iftekharuddin, K.M.: Feature-guided deep radiomics for glioblastoma patient survival prediction. Front. Neurosci. 13, 966 (2019). https:\/\/doi.org\/10.3389\/fnins.2019.00966. https:\/\/www.frontiersin.org\/article\/10.3389\/fnins.2019.00966","journal-title":"Front. Neurosci."},{"key":"4_CR18","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.compbiomed.2019.04.042","volume":"109","author":"Q Tong","year":"2019","unstructured":"Tong, Q., et al.: RIANet: recurrent interleaved attention network for cardiac MRI segmentation. Comput. Biol. Med. 109, 290\u2013302 (2019). https:\/\/doi.org\/10.1016\/j.compbiomed.2019.04.042","journal-title":"Comput. Biol. Med."},{"key":"4_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"4_CR20","doi-asserted-by":"publisher","unstructured":"Zhu, X., Yao, J., Huang, J.: Deep convolutional neural network for survival analysis with pathological images. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), vol. 1, pp. 544\u2013547. IEEE (2016). https:\/\/doi.org\/10.1109\/BIBM.2016.7822579","DOI":"10.1109\/BIBM.2016.7822579"}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59354-4_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:11:53Z","timestamp":1759270313000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59354-4_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030593537","9783030593544"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59354-4_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"1 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on PRedictive Intelligence In MEdicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"prime2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/basira-lab.com\/prime-miccai-2020\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","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":"17","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":"2","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":"85% - 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":"2-3","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":"3-4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The workshop was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}