{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T23:03:32Z","timestamp":1784070212606,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819237159","type":"print"},{"value":"9789819237166","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3716-6_13","type":"book-chapter","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T22:03:36Z","timestamp":1784066616000},"page":"162-173","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MORM: Multi-omics-Guided Region Mining and\u00a0Cross-Modal Interaction for\u00a0Multimodal Survival Prediction"],"prefix":"10.1007","author":[{"given":"Yan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiadi","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingchun","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoliang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Chen, R.J., 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"},{"issue":"8","key":"13_CR2","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","volume":"40","author":"RJ Chen","year":"2022","unstructured":"Chen, R.J., et al.: Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40(8), 865\u2013878 (2022)","journal-title":"Cancer Cell"},{"issue":"2","key":"13_CR3","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox, D.R.: Regression models and life-tables. J. Roy. Stat. Soc.: Ser. B (Methodol.) 34(2), 187\u2013202 (1972)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"13_CR4","doi-asserted-by":"publisher","first-page":"64479","DOI":"10.52202\/079017-2057","volume":"37","author":"K Hemker","year":"2024","unstructured":"Hemker, K., Simidjievski, N., Jamnik, M.: HEALNET: multimodal fusion for heterogeneous biomedical data. Adv. Neural. Inf. Process. Syst. 37, 64479\u201364498 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"13_CR5","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Jaume, G., Vaidya, A., Chen, R.J., Williamson, D.F., Liang, P.P., Mahmood, F.: Modeling dense multimodal interactions between biological pathways and histology for survival prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11579\u201311590 (2024)","DOI":"10.1109\/CVPR52733.2024.01100"},{"issue":"282","key":"13_CR7","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","volume":"53","author":"EL Kaplan","year":"1958","unstructured":"Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457\u2013481 (1958)","journal-title":"J. Am. Stat. Assoc."},{"issue":"1","key":"13_CR8","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(1), 24 (2018)","journal-title":"BMC Med. Res. Methodol."},{"issue":"22","key":"13_CR9","doi-asserted-by":"publisher","first-page":"3638","DOI":"10.3390\/cancers17223638","volume":"17","author":"AA Marouf","year":"2025","unstructured":"Marouf, A.A., Rokne, J.G., Alhajj, R.: Integrating multi-omics and medical imaging in artificial intelligence-based cancer research: an umbrella review of fusion strategies and applications. Cancers 17(22), 3638 (2025)","journal-title":"Cancers"},{"issue":"4","key":"13_CR10","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.1007\/s10278-024-01049-2","volume":"37","author":"A Parvaiz","year":"2024","unstructured":"Parvaiz, A., Nasir, E.S., Fraz, M.M.: From pixels to prognosis: a survey on AI-driven cancer patient survival prediction using digital histology images. J. Imaging Inf. Med. 37(4), 1728\u20131751 (2024)","journal-title":"J. Imaging Inf. Med."},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Raza, M., Azam, A., Qaiser, T., Rajpoot, N.: PS3: a multimodal transformer integrating pathology reports with histology images and biological pathways for cancer survival prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 22175\u201322186 (2025)","DOI":"10.1109\/ICCV51701.2025.02059"},{"key":"13_CR12","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: TRANSMIL: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural. Inf. Process. Syst. 34, 2136\u20132147 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, S., Lai, H., Huo, W., Zhang, Q.: Pomp: pathology-omics multimodal pre-training framework for cancer survival prediction. In: Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, pp. 7813\u20137821 (2025)","DOI":"10.24963\/ijcai.2025\/869"},{"issue":"10","key":"13_CR14","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein, J.N., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113\u20131120 (2013)","journal-title":"Nat. Genet."},{"key":"13_CR15","doi-asserted-by":"publisher","first-page":"1199087","DOI":"10.3389\/fgene.2023.1199087","volume":"14","author":"JS Wekesa","year":"2023","unstructured":"Wekesa, J.S., Kimwele, M.: A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment. Front. Genet. 14, 1199087 (2023)","journal-title":"Front. Genet."},{"key":"13_CR16","unstructured":"Xiang, J., Zhang, J.: Exploring low-rank property in multiple instance learning for whole slide image classification. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Xu, Y., Zhou, F., Zhao, C., Wang, Y., Yang, C., Chen, H.: Distilled prompt learning for incomplete multimodal survival prediction. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 5102\u20135111 (2025)","DOI":"10.1109\/CVPR52734.2025.00481"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: DTFD-MIL: double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18802\u201318812 (2022)","DOI":"10.1109\/CVPR52688.2022.01824"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3716-6_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T22:03:37Z","timestamp":1784066617000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3716-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"ISBN":["9789819237159","9789819237166"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3716-6_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]},"assertion":[{"value":"15 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}