{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T14:44:59Z","timestamp":1743000299339,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981403"},{"type":"electronic","value":"9789819981410"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8141-0_28","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:02:16Z","timestamp":1700902936000},"page":"374-386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Context Aware Lung Cancer Survival Prediction Network by\u00a0Using Whole Slide Images"],"prefix":"10.1007","author":[{"given":"Xinyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yicheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Sung, H., Ferlay, J., Siegel, R.L., et al.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J. Clin. 71(3), 209\u2013249 (2021)","DOI":"10.3322\/caac.21660"},{"key":"28_CR2","unstructured":"Pantanowitz, L., Valenstein, P.N., Evans, A.J., et al.: Clinical Statistics: Introducing Clinical Trials, Survival Analysis, and Longitudinal Data Analysis. CRC Press, 2nd ed. edn. (2018)"},{"key":"28_CR3","doi-asserted-by":"publisher","first-page":"36","DOI":"10.4103\/2153-3539.83746","volume":"2","author":"L Pantanowitz","year":"2011","unstructured":"Pantanowitz, L., Valenstein, P.N., Evans, A.J., et al.: Review of the current state of whole slide imaging in pathology. J Pathol Inform 2, 36 (2011)","journal-title":"J Pathol Inform"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Zhu, F., et al.: WSISA: making survival prediction from whole slide histopathological images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7234\u20137242 (2017)","DOI":"10.1109\/CVPR.2017.725"},{"issue":"1","key":"28_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-99269-x","volume":"12","author":"Y Li","year":"2022","unstructured":"Li, Y., Zhang, Y., Liang, X., et al.: Risk-aware survival time prediction from whole slide pathological images using deep learning. Sci. Rep. 12(1), 1\u201313 (2022)","journal-title":"Sci. Rep."},{"issue":"1","key":"28_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMI.2023.3307689","volume":"42","author":"L Fan","year":"2023","unstructured":"Fan, L., Sowmya, A., Meijering, E., et al.: Cancer survival prediction from whole slide images with self-supervised learning and slide consistency. IEEE Trans. Med. Imaging 42(1), 1\u201314 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"28_CR7","unstructured":"Li, Y., Yao, J., Xu, Z., et al.: Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. In: Medical Image Computing and Computer Assisted Intervention, pp. 290\u2013298 (2018)"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Yao, J., Zhu, X., Huang, J.: Deep multi-instance learning for survival prediction from whole slide images. In: Medical Image Computing and Computer-Assisted Intervention, pp. 505\u2013513 (2019)","DOI":"10.1007\/978-3-030-32239-7_55"},{"issue":"2","key":"28_CR9","doi-asserted-by":"publisher","first-page":"507","DOI":"10.3390\/cancers12020507","volume":"12","author":"WY Chuang","year":"2020","unstructured":"Chuang, W.Y., Chang, S.H., Yu, W.H., et al.: Successful identification of nasopharyngeal carcinoma in nasopharyngeal biopsies using deep learning. Cancers 12(2), 507 (2020)","journal-title":"Cancers"},{"issue":"10","key":"28_CR10","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N., Ocampo, P.S., Sakellaropoulos, T., et al.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018)","journal-title":"Nat. Med."},{"issue":"22","key":"28_CR11","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., Veta, M., Van Diest, P.J., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017)","journal-title":"JAMA"},{"issue":"1","key":"28_CR12","doi-asserted-by":"publisher","first-page":"10393","DOI":"10.1038\/s41598-018-27707-4","volume":"8","author":"S Wang","year":"2018","unstructured":"Wang, S., Chen, A., Yang, L., et al.: Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci. Rep. 8(1), 10393 (2018)","journal-title":"Sci. Rep."},{"issue":"14","key":"28_CR13","doi-asserted-by":"publisher","first-page":"i446","DOI":"10.1093\/bioinformatics\/btz342","volume":"35","author":"SCJ Parker","year":"2019","unstructured":"Parker, S.C.J., Khan, A., Talhouk, A., et al.: Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35(14), i446\u2013i454 (2019)","journal-title":"Bioinformatics"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Chen, R.J., Lu, M.Y., Weng, W.H., et al.: Multimodal co-attention transformer for survival prediction in gigapixel whole slide images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134025 (2021)","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"28_CR15","unstructured":"Chen, T., Kornblith, S., Norouzi, M., et al.: A simple framework for contrastive learning of visual representations, pp. 1597\u20131607 (2020)"},{"key":"28_CR16","first-page":"145","volume":"1","author":"G Salton","year":"1970","unstructured":"Salton, G., Wong, A.: Similarity measures. The SMART Retrieval Syst.: Exper. Autom. Document Process. 1, 145\u2013159 (1970)","journal-title":"The SMART Retrieval Syst.: Exper. Autom. Document Process."},{"issue":"1","key":"28_CR17","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., et al.: Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 24 (2018)","journal-title":"BMC Med. Res. Methodol."},{"issue":"2","key":"28_CR18","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"D Cox","year":"1972","unstructured":"Cox, D.: Regression models and life-tables. J. Roy. Stat. Soc.: Ser. B (Methodol.) 34(2), 187\u2013220 (1972)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"issue":"7407","key":"28_CR19","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1038\/nature11252","volume":"487","author":"T Network","year":"2012","unstructured":"Network, T.: Comprehensive molecular characterization of human colon and rectal cancer. Nature 487(7407), 330\u2013337 (2012)","journal-title":"Nature"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Team, N.L.S.T.R.: The national lung screening trial: overview and study design. Radiology 258(1), 243\u2013253 (2011)","DOI":"10.1148\/radiol.10091808"},{"key":"28_CR21","first-page":"971","volume":"30","author":"G Klambauer","year":"2017","unstructured":"Klambauer, G., Unterthiner, T., Mayr, A., et al.: Self-normalizing neural networks. Adv. Neural. Inf. Process. Syst. 30, 971\u2013980 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR22","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. ArXiv Preprint ArXiv:1412.6980, pp. 1\u201315 (2014)"},{"issue":"4","key":"28_CR23","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4","volume":"15","author":"FE Harrell Jr","year":"1996","unstructured":"Harrell, F.E., Jr., Lee, K.L., Mark, D.B.: Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15(4), 361\u2013387 (1996)","journal-title":"Stat. Med."},{"key":"28_CR24","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/978-3-030-87240-3_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, September 27 \u2013 October 1, 2021, Proceedings, Part V","author":"JR Chang","year":"2021","unstructured":"Chang, J.R., Lee, C.Y., Chen, C.C., Reischl, J., Qaiser, T., Yeh, C.Y.: Hybrid aggregation network for survival analysis from whole slide histopathological images. In: de Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, September 27 \u2013 October 1, 2021, Proceedings, Part V, pp. 731\u2013740. Springer International Publishing, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_70"},{"key":"28_CR25","doi-asserted-by":"publisher","first-page":"26022","DOI":"10.1109\/ACCESS.2019.2901049","volume":"7","author":"B Tang","year":"2019","unstructured":"Tang, B., Li, A., Li, B., et al.: Capsurv: capsule network for survival analysis with whole slide pathological images. IEEE Access 7, 26022\u201326030 (2019)","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8141-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T09:27:13Z","timestamp":1730626033000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8141-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981403","9789819981410"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8141-0_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}